{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from torchdyn.core import NeuralODE\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def damped_harmonic_oscillator(x0, v0, m, k, c, t_max=10, dt=0.1):\n",
    "    \"\"\"\n",
    "    Simulate the trajectory of a damped harmonic oscillator\n",
    "    simple newton method solver\n",
    "\n",
    "    Args:\n",
    "        x0 (float): Initial position\n",
    "        v0 (float): Initial velocity\n",
    "        m (float): Mass\n",
    "        k (float): Spring constant\n",
    "        c (float): Viscous force coefficient\n",
    "        t_max (float, optional): Maximum time to simulate (default: 10)\n",
    "        dt (float, optional): Time step (default: 0.01)\n",
    "\n",
    "    Returns:\n",
    "        numpy.ndarray: Array of position values\n",
    "        numpy.ndarray: Array of time values\n",
    "    \"\"\"\n",
    "    t = np.arange(0, t_max, dt)\n",
    "    x = np.zeros_like(t)\n",
    "    v = np.zeros_like(t)\n",
    "\n",
    "    x[0] = x0\n",
    "    v[0] = v0\n",
    "\n",
    "    for i in range(1, len(t)):\n",
    "        a = -c * v[i-1] / m - k * x[i-1] / m  # Acceleration\n",
    "        v[i] = v[i-1] + a * dt  # Update velocity\n",
    "        x[i] = x[i-1] + v[i-1] * dt  # Update position\n",
    "\n",
    "    return x, t\n",
    "\n",
    "def get_damping_zone(m,k,c):\n",
    "    zeta = c / (2 * np.sqrt(k * m))\n",
    "    if zeta > 1:\n",
    "        return \"over-damped\"\n",
    "    if zeta == 1:\n",
    "        return \"critically damped\"\n",
    "    if zeta < 1:\n",
    "        return \"under-damped\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for simplicity just have it produce trajectories across a range of viscosities\n",
    "# this is the only conditioned augmented dimension for the proof of concept\n",
    "\n",
    "x0 = 1.0  # Initial position\n",
    "v0 = 0.0  # Initial velocity\n",
    "m = 1.0   # Mass\n",
    "k = 1.0   # Spring constant\n",
    "c = np.array([0.25, 2, 3.75]) # np.arange(0.25,5.25,0.25)   # Viscous force coefficient\n",
    "t_max = 10\n",
    "\n",
    "X = []\n",
    "T = [] # same for all unnecessary\n",
    "labels = []\n",
    "\n",
    "\n",
    "for ci in c:\n",
    "    xi,ti = damped_harmonic_oscillator(x0, v0, m, k, ci, t_max)\n",
    "    labeli = get_damping_zone(m,k,ci)\n",
    "    X.append(xi)\n",
    "    T.append(ti)\n",
    "    labels.append(labeli)\n",
    "\n",
    "\n",
    "colors = ['red', 'green', 'blue']\n",
    "for i, x in enumerate(X):\n",
    "    labeli = labels[i]\n",
    "    plt.plot(T[i], x, label=labeli, color=colors[i])\n",
    "\n",
    "# for i, x in enumerate(X):\n",
    "#     labeli = labels[i]\n",
    "#     plt.plot(T[i], x, label=labeli)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Position')\n",
    "plt.title(f'Harmonic Oscillator')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create \"HADM_ID\" column to label the three distinct oscillation groups\n",
    "hadm_id = [1]*100 + [2]*100 + [3]*100  # Repeats 1, 2, and 3, each 100 times\n",
    "\n",
    "# Scale time values T to range between 0 and 1\n",
    "T = [t/10 for t in T]\n",
    "\n",
    "# # Identify indices where oscillation changes (i.e., where HADM_ID changes)\n",
    "# end_pt_indices = torch.nonzero(hadm_id[1:] != hadm_id[:-1], as_tuple=False).squeeze() + 1\n",
    "\n",
    "# Create a DataFrame with the relevant columns\n",
    "# for this dataset, normalization of x is skipped\n",
    "df = pd.DataFrame({\n",
    "    'HADM_ID': hadm_id,\n",
    "    'x': torch.tensor(np.concatenate(X), dtype=torch.float32).numpy(),\n",
    "    'c': torch.tensor(c).repeat_interleave(100).type(torch.float32).numpy(),\n",
    "    't': torch.tensor(np.concatenate(T), dtype=torch.float32).numpy()\n",
    "})\n",
    "\n",
    "# # Prepare data dictionary for PyTorch Lightning\n",
    "# # for this overfitting demo, using the same data for train/val/test\n",
    "df_all = {'train': df, 'val': df, 'test': df}\n",
    "\n",
    "file_path = \"3Oscillation_data.pkl\"\n",
    "with open(file_path, 'wb') as f:\n",
    "    pickle.dump(df_all, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "class TrainingDataset(Dataset):\n",
    "    def __init__(self, x0_values, x0_classes, x1_values, times_x0, times_x1):\n",
    "        self.x0_values = x0_values\n",
    "        self.x0_classes = x0_classes\n",
    "        self.x1_values = x1_values\n",
    "        self.times_x0 = times_x0\n",
    "        self.times_x1 = times_x1\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x0_values)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return (self.x0_values[idx], self.x0_classes[idx], self.x1_values[idx], self.times_x0[idx], self.times_x1[idx])\n",
    "\n",
    "\n",
    "class PatientDataset(Dataset):\n",
    "    def __init__(self, patient_data):\n",
    "        self.patient_data = patient_data\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.patient_data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.patient_data[idx]\n",
    "\n",
    "\n",
    "class eICUDataLoader:\n",
    "    def __init__(self, \n",
    "                 file_path, \n",
    "                 t_headings,\n",
    "                 x_headings,\n",
    "                 cond_headings,\n",
    "                 memory = 0,\n",
    "                 batch_size=256, \n",
    "                 groupby = 'HADM_ID',\n",
    "                 train_consecutive=False):\n",
    "        \n",
    "        self.batch_size = batch_size\n",
    "        self.file_path = file_path\n",
    "        self.x_headings = x_headings\n",
    "        self.cond_headings = cond_headings\n",
    "        self.t_headings = t_headings\n",
    "        self.input_dim = len(self.x_headings) + len(self.cond_headings)\n",
    "        self.output_dim = len(self.x_headings)\n",
    "        self.memory = memory\n",
    "        self.min_timept = 5 + self.memory\n",
    "        self.train_consecutive = train_consecutive\n",
    "        self.groupby = groupby\n",
    "\n",
    "        # Load and setup data\n",
    "        self.data = pd.read_pickle(self.file_path)\n",
    "        self.train_data = self.__filter_data(self.data['train'])\n",
    "        self.val_data = self.__filter_data(self.data['val'])\n",
    "        self.test_data = self.__filter_data(self.data['test'])\n",
    "\n",
    "    def __filter_data(self, data_set):\n",
    "        return data_set.groupby(self.groupby).filter(lambda x: len(x) > self.min_timept)\n",
    "\n",
    "    def __unpack__(self, data_set):\n",
    "        x = data_set[self.x_headings].values\n",
    "        cond = data_set[self.cond_headings].values\n",
    "        t = data_set[self.t_headings].values\n",
    "        return x, cond, t\n",
    "    \n",
    "    def __sort_group__(self, data_set):\n",
    "        grouped = data_set.groupby(self.groupby)\n",
    "        grouped_sorted = grouped.apply(lambda x: x.sort_values([self.t_headings], ascending=True)).reset_index(drop=True)\n",
    "        return grouped_sorted\n",
    "\n",
    "    def create_pairs(self, df):\n",
    "        x0_values = []\n",
    "        x0_classes = []\n",
    "        x1_values = []\n",
    "        times_x0 = []\n",
    "        times_x1 = []\n",
    "\n",
    "        for _, group in df.groupby(self.groupby):\n",
    "            sorted_group = group.sort_values(by=self.t_headings)\n",
    "\n",
    "            for i in range(self.memory, len(sorted_group) - 1):\n",
    "                x0 = sorted_group.iloc[i]\n",
    "                x0_class = x0[self.cond_headings].values\n",
    "                x0_value = x0[self.x_headings].values\n",
    "\n",
    "                x1 = sorted_group.iloc[i + 1]\n",
    "                x1_value = x1[self.x_headings].values\n",
    "\n",
    "                if self.memory > 0:\n",
    "                    x0_memory = sorted_group.iloc[i - self.memory:i]\n",
    "                    x0_memory_flatten = x0_memory[self.x_headings].values.flatten()\n",
    "                    x0_class = np.append(x0_class, x0_memory_flatten)\n",
    "\n",
    "                x0_values.append(x0_value)\n",
    "                x0_classes.append(x0_class)\n",
    "                x1_values.append(x1_value)\n",
    "                times_x0.append(x0[self.t_headings])\n",
    "                times_x1.append(x1[self.t_headings])\n",
    "\n",
    "        x0_values = np.array(x0_values).squeeze().astype(np.float32)\n",
    "        x0_classes = np.array(x0_classes).squeeze().astype(np.float32)\n",
    "        x1_values = np.array(x1_values).squeeze().astype(np.float32)\n",
    "        times_x0 = np.array(times_x0).squeeze().astype(np.float32)\n",
    "        times_x1 = np.array(times_x1).squeeze().astype(np.float32)\n",
    "\n",
    "        # if ==0, shape of (bs, 0) will be kept; \n",
    "        # if >=2, shape of (bs, cond_num+dim*memory) will be kept\n",
    "        if len(self.cond_headings) + self.memory == 1: \n",
    "            x0_classes = np.expand_dims(x0_classes, axis=1)\n",
    "        \n",
    "        if len(self.x_headings) < 2:\n",
    "            x0_values = np.expand_dims(x0_values, axis=1)\n",
    "            x1_values = np.expand_dims(x1_values, axis=1)\n",
    "\n",
    "        return x0_values, x0_classes, x1_values, times_x0, times_x1\n",
    "\n",
    "    def create_patient_data(self, df):\n",
    "        patient_lst = []\n",
    "        for _, group in df.groupby(self.groupby):\n",
    "            sorted_group = group.sort_values(by=self.t_headings)\n",
    "            x0_values, x0_classes, x1_values, times_x0, times_x1 = self.create_pairs(sorted_group)\n",
    "\n",
    "            patient_lst.append((x0_values,\n",
    "                                x0_classes,\n",
    "                                x1_values,\n",
    "                                times_x0,\n",
    "                                times_x1))\n",
    "        return patient_lst\n",
    "\n",
    "    def create_patient_data_t0(self, df):\n",
    "        patient_lst = []\n",
    "        for _, group in df.groupby(self.groupby):\n",
    "            sorted_group = group.sort_values(by=self.t_headings)\n",
    "            x0_values, x0_classes, x1_values, times_x0, times_x1 = self.create_pairs(sorted_group)\n",
    "\n",
    "            if len(self.cond_headings) < 2:\n",
    "                x0_classes = np.expand_dims(x0_classes, axis=1)\n",
    "            else:\n",
    "                x0_classes = x0_classes.squeeze().astype(np.float32)\n",
    "\n",
    "            x0_values = np.repeat(x0_values[0][None, :], len(x0_values), axis=0)\n",
    "            x0_classes = np.repeat(x0_classes[0][None, :], len(x0_values), axis=0)\n",
    "            times_x0 = np.repeat(times_x0[0], len(x0_values))\n",
    "\n",
    "            patient_lst.append((x0_values.squeeze().astype(np.float32),\n",
    "                                x0_classes,\n",
    "                                x1_values.squeeze().astype(np.float32),\n",
    "                                times_x0.squeeze().astype(np.float32),\n",
    "                                times_x1.squeeze().astype(np.float32)))\n",
    "        return patient_lst\n",
    "\n",
    "    def get_dataloader(self, data, shuffle=True, for_training=True):\n",
    "        if for_training and self.train_consecutive:\n",
    "            data = self.create_patient_data_t0(data)\n",
    "            dataset = PatientDataset(data)\n",
    "            return DataLoader(dataset, batch_size=1, shuffle=shuffle, num_workers=0)\n",
    "        elif for_training:\n",
    "            data = self.create_pairs(data)\n",
    "            dataset = TrainingDataset(*data)\n",
    "            return DataLoader(dataset, batch_size=self.batch_size, shuffle=shuffle, num_workers=0)\n",
    "        else:\n",
    "            data = self.create_patient_data(data)\n",
    "            dataset = PatientDataset(data)\n",
    "            return DataLoader(dataset, batch_size=1, shuffle=shuffle, num_workers=0)\n",
    "\n",
    "    def get_train_loader(self, shuffle=True):\n",
    "        return self.get_dataloader(self.train_data, shuffle=shuffle, for_training=True)\n",
    "\n",
    "    def get_val_loader(self):\n",
    "        return self.get_dataloader(self.val_data, shuffle=False, for_training=False)\n",
    "\n",
    "    def get_test_loader(self):\n",
    "        return self.get_dataloader(self.test_data, shuffle=False, for_training=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class torch_wrapper_tv(torch.nn.Module):\n",
    "    \"\"\"Wraps model to torchdyn compatible format.\"\"\"\n",
    "\n",
    "    def __init__(self, model):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "\n",
    "    def forward(self, t, x, *args, **kwargs):\n",
    "        return self.model(torch.cat([x, t.repeat(x.shape[0])[:, None]], 1))\n",
    "\n",
    "\n",
    "class SDE(torch.nn.Module):\n",
    "\n",
    "    noise_type = \"diagonal\"\n",
    "    sde_type = \"ito\"\n",
    "\n",
    "    # noise is sigma in this notebook for the equation sigma * (t * (1 - t))\n",
    "    def __init__(self, ode_drift, noise=1.0, reverse=False):\n",
    "        super().__init__()\n",
    "        self.drift = ode_drift\n",
    "        self.reverse = reverse\n",
    "        self.noise = noise\n",
    "\n",
    "    # Drift\n",
    "    def f(self, t, y):\n",
    "        if self.reverse:\n",
    "            t = 1 - t\n",
    "        if len(t.shape) == len(y.shape):\n",
    "            x = torch.cat([y, t], 1)\n",
    "        else:\n",
    "            x = torch.cat([y, t.repeat(y.shape[0])[:, None]], 1)\n",
    "        return self.drift(x)\n",
    "\n",
    "    # Diffusion\n",
    "    def g(self, t, y):\n",
    "        return torch.ones_like(t) * torch.ones_like(y) * self.noise\n",
    "\n",
    "\n",
    "class SDE_func_solver(torch.nn.Module):\n",
    "\n",
    "    noise_type = \"diagonal\"\n",
    "    sde_type = \"ito\"\n",
    "\n",
    "    # noise is sigma in this notebook for the equation sigma * (t * (1 - t))\n",
    "    def __init__(self, ode_drift, noise, reverse=False):\n",
    "        super().__init__()\n",
    "        self.drift = ode_drift\n",
    "        self.reverse = reverse\n",
    "        self.noise = noise # changeable, a model itself\n",
    "\n",
    "    # Drift\n",
    "    def f(self, t, y):\n",
    "        if self.reverse:\n",
    "            t = 1 - t\n",
    "        if len(t.shape) == len(y.shape):\n",
    "            x = torch.cat([y, t], 1)\n",
    "        else:\n",
    "            x = torch.cat([y, t.repeat(y.shape[0])[:, None]], 1)\n",
    "        return self.drift(x)\n",
    "\n",
    "    # Diffusion\n",
    "    def g(self, t, y):\n",
    "        if self.reverse:\n",
    "            t = 1 - t\n",
    "        if len(t.shape) == len(y.shape):\n",
    "            x = torch.cat([y, t], 1)\n",
    "        else:\n",
    "            x = torch.cat([y, t.repeat(y.shape[0])[:, None]], 1)\n",
    "        noise_result = self.noise(x)\n",
    "        return noise_result* torch.sqrt(t * (1 - t))\n",
    "    \n",
    "\n",
    "def metrics_calculation(pred, true, metrics=['mse_loss'], cutoff=-0.91, map_idx = 1):\n",
    "    \n",
    "    # if pred is a tensor, convert to numpy\n",
    "    if isinstance(pred, torch.Tensor):\n",
    "        pred = pred.detach().cpu().squeeze().numpy()\n",
    "        true = true.detach().cpu().squeeze().numpy()\n",
    "\n",
    "    loss_D = {key : None for key in metrics}\n",
    "    for metric in metrics:\n",
    "        if metric == 'mse_loss':\n",
    "            loss_D['mse_loss'] = np.mean((pred - true)**2)\n",
    "            # self.log('mse_loss', self.loss_fn(pred, true))\n",
    "        if metric == 'l1_loss':\n",
    "            loss_D['l1_loss'] = np.mean(np.abs(pred - true))\n",
    "            # self.log('l1_loss', torch.mean(torch.abs(pred - true)))\n",
    "\n",
    "    return loss_D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import math\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from torchdyn.core import NeuralODE\n",
    "from torch import optim\n",
    "import torch.functional as F\n",
    "\n",
    "\n",
    "PE_BASE = 0.012 # 0.012615662610100801\n",
    "NUM_FREQS = 10\n",
    "\n",
    "def positional_encoding_tensor(time_tensor, num_frequencies=NUM_FREQS, base=PE_BASE):\n",
    "    # Ensure the time tensor is in the range [0, 1]\n",
    "    time_tensor = time_tensor.clamp(0, 1).unsqueeze(1)  # Clamp and add dimension for broadcasting\n",
    "\n",
    "    # Compute the arguments for the sine and cosine functions using the custom base\n",
    "    frequencies = torch.pow(base, -torch.arange(0, num_frequencies, dtype=torch.float32) / num_frequencies)\n",
    "    angles = time_tensor * frequencies\n",
    "\n",
    "    # Compute the sine and cosine for even and odd indices respectively\n",
    "    sine = torch.sin(angles)\n",
    "    cosine = torch.cos(angles)\n",
    "\n",
    "    # Stack them along the last dimension\n",
    "    pos_encoding = torch.stack((sine, cosine), dim=-1)\n",
    "    pos_encoding = pos_encoding.flatten(start_dim=2)\n",
    "\n",
    "    # Normalize to have values between 0 and 1\n",
    "    pos_encoding = (pos_encoding + 1) / 2  # Now values are between 0 and 1\n",
    "    \n",
    "    return pos_encoding\n",
    "\n",
    "class MLP_conditional_memory(torch.nn.Module):\n",
    "    \"\"\" Conditional with many available classes\n",
    "\n",
    "    return the class as is\n",
    "    \"\"\"\n",
    "    def __init__(self, \n",
    "                 dim, \n",
    "                 treatment_cond,\n",
    "                 memory, # how many time steps\n",
    "                 out_dim=None, \n",
    "                 w=64, \n",
    "                 time_varying=False, \n",
    "                 conditional=False,  \n",
    "                 time_dim = NUM_FREQS * 2,\n",
    "                 clip = None,\n",
    "                 ):\n",
    "        super().__init__()\n",
    "        self.time_varying = time_varying\n",
    "        if out_dim is None:\n",
    "            self.out_dim = dim \n",
    "        self.out_dim += 1 # for the time dimension\n",
    "        self.treatment_cond = treatment_cond\n",
    "        self.memory = memory\n",
    "        self.dim = dim\n",
    "        self.indim = dim + (time_dim if time_varying else 0) + (self.treatment_cond if conditional else 0) + (dim * memory)\n",
    "        self.net = torch.nn.Sequential(\n",
    "            torch.nn.Linear(self.indim, w),\n",
    "            torch.nn.SELU(),\n",
    "            torch.nn.Linear(w, w),\n",
    "            torch.nn.SELU(),\n",
    "            torch.nn.Linear(w, w),\n",
    "            torch.nn.SELU(),\n",
    "            torch.nn.Linear(w,self.out_dim),\n",
    "        )\n",
    "        self.default_class = 0\n",
    "        self.clip = clip\n",
    "\n",
    "    def encoding_function(self, time_tensor):\n",
    "        return positional_encoding_tensor(time_tensor)    \n",
    "\n",
    "    def forward_train(self, x):\n",
    "        \"\"\"forward pass\n",
    "        Assume first two dimensions are x, c, then t\n",
    "        \"\"\"\n",
    "        time_tensor = x[:,-1]\n",
    "        encoded_time_span = self.encoding_function(time_tensor).reshape(-1, NUM_FREQS * 2)\n",
    "        new_x = torch.cat([x[:,:-1], encoded_time_span], dim=1)\n",
    "        result = self.net(new_x)\n",
    "        return torch.cat([result[:,:-1], x[:,self.dim:-1], result[:,-1].unsqueeze(1)], dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\" call forward_train for training\n",
    "            x here is x_t\n",
    "            xt = (t)x1 + (1-t)x0\n",
    "            (xt - tx1)/(1-t) = x0\n",
    "        \"\"\"\n",
    "        x1 = self.forward_train(x)\n",
    "        x1_coord = x1[:,:self.dim]\n",
    "        t = x[:,-1]\n",
    "        pred_time_till_t1 = x1[:,-1]\n",
    "        x_coord = x[:,:self.dim]\n",
    "        if self.clip is None:\n",
    "            vt = (x1_coord - x_coord)/(pred_time_till_t1)\n",
    "        else:\n",
    "            vt = (x1_coord - x_coord)/torch.clip((pred_time_till_t1),min=self.clip)\n",
    "\n",
    "        final_vt = torch.cat([vt, torch.zeros_like(x[:,self.dim:-1])], dim=1)\n",
    "        return final_vt\n",
    "\n",
    "class MLP_conditional_memory_sde_noise(torch.nn.Module):\n",
    "    \"\"\" Conditional with many available classes\n",
    "\n",
    "    return the class as is\n",
    "    \"\"\"\n",
    "    def __init__(self, \n",
    "                 dim, \n",
    "                 treatment_cond,\n",
    "                 memory, # how many time steps\n",
    "                 out_dim=None, \n",
    "                 w=64, \n",
    "                 time_varying=False, \n",
    "                 conditional=False,  \n",
    "                 time_dim = NUM_FREQS * 2,\n",
    "                 clip = None,\n",
    "                 ):\n",
    "        super().__init__()\n",
    "        self.time_varying = time_varying\n",
    "        if out_dim is None:\n",
    "            self.out_dim = 1 # for noise \n",
    "        self.treatment_cond = treatment_cond\n",
    "        self.memory = memory\n",
    "        self.dim = dim\n",
    "        self.indim = dim + (time_dim if time_varying else 0) + (self.treatment_cond if conditional else 0) + (dim * memory)\n",
    "        self.net = torch.nn.Sequential(\n",
    "            torch.nn.Linear(self.indim, w),\n",
    "            torch.nn.SELU(),\n",
    "            torch.nn.Linear(w, w),\n",
    "            torch.nn.SELU(),\n",
    "            torch.nn.Linear(w, w),\n",
    "            torch.nn.SELU(),\n",
    "            torch.nn.Linear(w,self.out_dim),\n",
    "        )\n",
    "        self.default_class = 0\n",
    "        self.clip = clip\n",
    "\n",
    "    def encoding_function(self, time_tensor):\n",
    "        return positional_encoding_tensor(time_tensor)    \n",
    "\n",
    "    def forward_train(self, x):\n",
    "        \"\"\"forward pass\n",
    "        Assume first two dimensions are x, c, then t\n",
    "        \"\"\"\n",
    "        time_tensor = x[:,-1]\n",
    "        encoded_time_span = self.encoding_function(time_tensor).reshape(-1, NUM_FREQS * 2)\n",
    "        new_x = torch.cat([x[:,:-1], encoded_time_span], dim=1)\n",
    "        result = self.net(new_x)\n",
    "        return result\n",
    "    \n",
    "    def forward(self,x):\n",
    "        result = self.forward_train(x)\n",
    "        return torch.cat([result, torch.zeros_like(x[:,1:-1])], dim=1)\n",
    "\n",
    "def mse_loss(pred, true):\n",
    "    return torch.mean((pred - true) ** 2)\n",
    "\n",
    "class Noise_MLP_Cond_Memory_Module(torch.nn.Module):\n",
    "    def __init__(self, treatment_cond, memory=3, dim=2, w=64, time_varying=True, conditional=True, lr=1e-6, sigma=0.1, \n",
    "                 loss_fn=mse_loss, metrics=['mse_loss', 'l1_loss'], implementation=\"ODE\", sde_noise=0.1, clip=None, naming=None):\n",
    "        super().__init__()\n",
    "        self.flow_model = MLP_conditional_memory(dim=dim, w=w, time_varying=time_varying, conditional=conditional, \n",
    "                                                 treatment_cond=treatment_cond, memory=memory, clip=clip)\n",
    "        if implementation == \"SDE\":\n",
    "            self.noise_model = MLP_conditional_memory_sde_noise(dim=dim, w=w, time_varying=time_varying, conditional=conditional, \n",
    "                                                                treatment_cond=treatment_cond, memory=memory, clip=clip)\n",
    "        else:\n",
    "            self.noise_model = MLP_conditional_memory(dim=dim, w=w, time_varying=time_varying, conditional=conditional, \n",
    "                                                      treatment_cond=treatment_cond, memory=memory, clip=clip)\n",
    "        self.loss_fn = loss_fn\n",
    "        self.dim = dim\n",
    "        self.w = w\n",
    "        self.time_varying = time_varying\n",
    "        self.conditional = conditional\n",
    "        self.treatment_cond = treatment_cond\n",
    "        self.lr = lr\n",
    "        self.sigma = sigma\n",
    "        self.metrics = metrics\n",
    "        self.implementation = implementation\n",
    "        self.memory = memory\n",
    "        self.sde_noise = sde_noise\n",
    "        self.clip = clip\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.flow_model(x)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        flow_optimizer = torch.optim.Adam(self.flow_model.parameters(), lr=self.lr)\n",
    "        noise_optimizer = torch.optim.Adam(self.noise_model.parameters(), lr=self.lr)\n",
    "        return flow_optimizer, noise_optimizer\n",
    "\n",
    "    def __convert_tensor__(self, tensor):\n",
    "        return tensor.to(torch.float32)\n",
    "\n",
    "    def __x_processing__(self, x0, x1, t0, t1):\n",
    "        t = torch.rand(x0.shape[0], 1).to(x0.device)\n",
    "        mu_t = x0 * (1 - t) + x1 * t\n",
    "        data_t_diff = (t1 - t0).unsqueeze(1)\n",
    "        x = mu_t + self.sigma * torch.randn(x0.shape[0], self.dim).to(x0.device)\n",
    "        ut = (x1 - x0) / (data_t_diff + 1e-4)\n",
    "        t_model = t * data_t_diff + t0.unsqueeze(1)\n",
    "        futuretime = t1 - t_model\n",
    "        return x, ut, t_model, futuretime, t\n",
    "\n",
    "class MLP_Cond_Memory_Module(torch.nn.Module):\n",
    "    def __init__(self, treatment_cond, memory=3, dim=2, w=64, time_varying=True, conditional=True, lr=1e-6, sigma=0.1, \n",
    "                 loss_fn=mse_loss, metrics=['mse_loss', 'l1_loss'], implementation=\"ODE\", sde_noise=0.1, clip=None, naming=None):\n",
    "        super().__init__()\n",
    "        self.model = MLP_conditional_memory(dim=dim, w=w, time_varying=time_varying, conditional=conditional, \n",
    "                                            treatment_cond=treatment_cond, memory=memory, clip=clip)\n",
    "        self.loss_fn = loss_fn\n",
    "        self.dim = dim\n",
    "        self.w = w\n",
    "        self.time_varying = time_varying\n",
    "        self.conditional = conditional\n",
    "        self.treatment_cond = treatment_cond\n",
    "        self.lr = lr\n",
    "        self.sigma = sigma\n",
    "        self.metrics = metrics\n",
    "        self.implementation = implementation\n",
    "        self.memory = memory\n",
    "        self.sde_noise = sde_noise\n",
    "        self.clip = clip\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)\n",
    "        return optimizer\n",
    "\n",
    "    def __convert_tensor__(self, tensor):\n",
    "        return tensor.to(torch.float32)\n",
    "\n",
    "    def __x_processing__(self, x0, x1, t0, t1):\n",
    "        t = torch.rand(x0.shape[0], 1).to(x0.device)\n",
    "        mu_t = x0 * (1 - t) + x1 * t\n",
    "        data_t_diff = (t1 - t0).unsqueeze(1)\n",
    "        x = mu_t + self.sigma * torch.randn(x0.shape[0], self.dim).to(x0.device)\n",
    "        ut = (x1 - x0) / (data_t_diff + 1e-4)\n",
    "        t_model = t * data_t_diff + t0.unsqueeze(1)\n",
    "        futuretime = t1 - t_model\n",
    "        return x, ut, t_model, futuretime, t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, noise_prediction, train_loader, val_loader=None, num_epochs=10, device='cuda'):\n",
    "    model.to(device)\n",
    "\n",
    "    # with noise prediction\n",
    "    if noise_prediction: \n",
    "        flow_optimizer, noise_optimizer = model.configure_optimizers()\n",
    "    # without noise prediction\n",
    "    else: \n",
    "        optimizer = model.configure_optimizers()\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        train_loss = 0\n",
    "        for batch in train_loader:\n",
    "            x0, x0_class, x1, x0_time, x1_time = [b.to(device) for b in batch]\n",
    "\n",
    "            x, ut, t_model, futuretime, t = model.__x_processing__(x0, x1, x0_time, x1_time)\n",
    "\n",
    "            in_tensor = torch.cat([x, x0_class, t_model], dim=-1)\n",
    "\n",
    "            # with noise prediction\n",
    "            if noise_prediction: \n",
    "                xt = model.model.flow_model.forward_train(in_tensor)\n",
    "\n",
    "                if model.implementation == \"SDE\":\n",
    "                    sde_noise = model.noise_model.forward_train(in_tensor)\n",
    "                    variance = torch.sqrt(t * (1 - t)) * sde_noise\n",
    "                    noise = torch.randn_like(xt[:, :model.dim]) * variance\n",
    "                    loss = model.loss_fn(xt[:, :model.dim] + noise.clone().detach(), x1) + model.loss_fn(xt[:, -1], futuretime)\n",
    "                    uncertainty = (xt[:, :model.dim].clone().detach() + noise)\n",
    "                    noise_loss = model.loss_fn(uncertainty, x1)\n",
    "                else:\n",
    "                    loss = model.loss_fn(xt[:, :model.dim], x1) + model.loss_fn(xt[:, -1], futuretime)\n",
    "                    uncertainty = torch.abs(xt[:, :model.dim].clone().detach() - x1)\n",
    "                    noise_loss = model.loss_fn(model.noise_model.forward_train(in_tensor)[:, :model.dim], uncertainty)\n",
    "\n",
    "                flow_optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                flow_optimizer.step()\n",
    "\n",
    "                noise_optimizer.zero_grad()\n",
    "                noise_loss.backward()\n",
    "                noise_optimizer.step()\n",
    "\n",
    "                train_loss += (loss + noise_loss).item()\n",
    "\n",
    "            # without noise prediction\n",
    "            else: \n",
    "                xt = model.model.forward_train(in_tensor)\n",
    "\n",
    "                if model.implementation == \"SDE\":\n",
    "                    variance = t * (1 - t) * model.sde_noise\n",
    "                    noise = torch.randn_like(xt[:, :model.dim]) * torch.sqrt(variance)\n",
    "                    loss = model.loss_fn(xt[:, :model.dim] + noise, x1) # + model.loss_fn(xt[:, -1], futuretime)\n",
    "                else:\n",
    "                    loss = model.loss_fn(xt[:, :model.dim], x1) # + model.loss_fn(xt[:, -1], futuretime)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                train_loss += loss.item()\n",
    "\n",
    "        # validation\n",
    "        if (epoch+1) % 100 == 0:\n",
    "            print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {train_loss/len(train_loader)}\")\n",
    "            if val_loader: \n",
    "                test_model(model, noise_prediction, val_loader, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(model, noise_prediction, test_loader, device):\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    list_of_pairs = []\n",
    "    list_of_times = []\n",
    "\n",
    "    dict_full_trajs = {}\n",
    "    dict_pred_trajs = {}\n",
    "\n",
    "    loss_sum = 0\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, batch in enumerate(test_loader):\n",
    "            batch = [x.to(device) for x in batch]\n",
    "            loss, pairs, metricD, noise_loss, noise_pair, full_time = test_func_step(batch, batch_idx, model, noise_prediction)\n",
    "            list_of_pairs.append(pairs)\n",
    "            list_of_times.append(full_time)\n",
    "            full_traj = pairs[0][0]\n",
    "            pred_traj = pairs[0][1]\n",
    "            dict_full_trajs[batch_idx] = full_traj.detach().cpu().numpy()\n",
    "            dict_pred_trajs[batch_idx] = pred_traj.detach().cpu().numpy()\n",
    "            loss_sum += loss\n",
    "\n",
    "        fig = plt.figure(figsize=(5, 4))\n",
    "        ax = fig.add_subplot(111)\n",
    "        colors = ['red', 'green', 'blue']\n",
    "        T = np.arange(0,1,0.01)\n",
    "        count = 0\n",
    "        for _,v in dict_pred_trajs.items():\n",
    "            ax.plot(T[model.memory:], v, label=\"predicted\" if count == 0 else '', color=colors[count])\n",
    "            count += 1\n",
    "        count = 0\n",
    "        for _,v in dict_full_trajs.items():\n",
    "            ax.plot(T[model.memory:], v, label=\"ground truth\" if count == 0 else '', color=colors[count], linestyle='--')\n",
    "            count += 1\n",
    "\n",
    "        ax.set_xlabel('scaled t', fontsize='10')\n",
    "        ax.set_ylabel('x', fontsize='10')\n",
    "        # ax.set_title(\"damped_oscillations_epoch_{}\".format(self.current_epoch))\n",
    "        ax.legend(fontsize='10')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "    print(f\"validation loss: {loss_sum/len(test_loader)}\")\n",
    "\n",
    "def test_func_step(batch, batch_idx, model, noise_prediction):\n",
    "    \"\"\"Assuming each batch is one patient\"\"\"\n",
    "    total_loss = []\n",
    "    traj_pairs = []\n",
    "\n",
    "    total_noise_loss = []\n",
    "    noise_pairs = []\n",
    "\n",
    "    x0_values, x0_classes, x1_values, times_x0, times_x1 = batch\n",
    "    times_x0 = times_x0.squeeze()\n",
    "    times_x1 = times_x1.squeeze()\n",
    "\n",
    "    full_traj = torch.cat([x0_values[0, 0, :model.dim].unsqueeze(0), x1_values[0, :, :model.dim]], dim=0)\n",
    "    full_time = torch.cat([times_x0[0].unsqueeze(0), times_x1], dim=0)\n",
    "    # print(f\"full_traj {full_traj.shape}\")\n",
    "    # print(f\"full_time {full_time.shape}\")\n",
    "\n",
    "    if model.implementation == \"ODE\":\n",
    "        ind_loss, pred_traj, noise_mse, noise_pred = test_trajectory_ode(batch, model, noise_prediction)\n",
    "    elif model.implementation == \"SDE\":\n",
    "        ind_loss, pred_traj, noise_mse, noise_pred = test_trajectory_sde(batch, model, noise_prediction)\n",
    "\n",
    "    # print(f\"pred_traj {pred_traj.shape}\")\n",
    "\n",
    "    total_loss.append(ind_loss)\n",
    "    traj_pairs.append([full_traj, pred_traj])\n",
    "    noise_pairs.append([full_traj, noise_pred])\n",
    "    total_noise_loss.append(noise_mse)\n",
    "\n",
    "    # Optionally detach and move tensors to CPU for further processing or visualization\n",
    "    full_traj = full_traj.detach().cpu().numpy()\n",
    "    pred_traj = pred_traj.detach().cpu().numpy()\n",
    "    full_time = full_time.detach().cpu().numpy()\n",
    "\n",
    "    # # Generate the plot\n",
    "    # fig = plot_3d_path_ind_noise(pred_traj, full_traj, noise_pred, t_span=full_time, title=f\"trajectory_patient_{batch_idx}\")\n",
    "    \n",
    "    # plt.close(fig)\n",
    "\n",
    "    # Calculate metrics\n",
    "    metricD = metrics_calculation(pred_traj, full_traj)\n",
    "    return np.mean(total_loss), traj_pairs, metricD, np.mean(total_noise_loss), noise_pairs, full_time\n",
    "    \n",
    "def test_trajectory_ode(batch, model, noise_prediction): # have to squeeze here to adjust for bs=1\n",
    "    # with noise prediction\n",
    "    \n",
    "    if noise_prediction:\n",
    "        node = NeuralODE(torch_wrapper_tv(model.flow_model), solver=\"dopri5\", sensitivity=\"adjoint\", atol=1e-4, rtol=1e-4)\n",
    "        node_noise = NeuralODE(torch_wrapper_tv(model.noise_model), solver=\"dopri5\", sensitivity=\"adjoint\", atol=1e-4, rtol=1e-4)\n",
    "    else:\n",
    "        node = NeuralODE(torch_wrapper_tv(model.model), solver=\"dopri5\", sensitivity=\"adjoint\", atol=1e-4, rtol=1e-4)\n",
    "    total_pred, noise_pred = [], []\n",
    "    mse, noise_mse = [], []\n",
    "    total_pred = []\n",
    "    mse = []\n",
    "\n",
    "    x0_values, x0_classes, x1_values, times_x0, times_x1 = batch\n",
    "    # print(\"=\"*10)\n",
    "    # print(\"HERE test_trajectory ode\")\n",
    "    # print(f\"x0 from dataloader {x0_values.shape}\")\n",
    "    # print(f\"cond from dataloader {x0_classes.shape}\")\n",
    "    # print(f\"x1 from dataloader {x1_values.shape}\")\n",
    "    # print(f\"t0 from dataloader {times_x0.shape}\")\n",
    "    # print(f\"t1 from dataloader {times_x1.shape}\")\n",
    "    # print(f\"t0[0] {times_x0[0].shape}\")\n",
    "    # print(f\"t1[0] {times_x1[0].shape}\")\n",
    "    # print(f\"history {x0_classes[0][-(model.memory * model.dim):].shape}\")\n",
    "    # print(f\"x0_values.shape[0] {x0_values.shape[0]}\")\n",
    "    # print(f\"x1_values.shape[0] {x1_values.shape[0]}\")\n",
    "    x0_values = x0_values.squeeze(0)\n",
    "    x0_classes = x0_classes.squeeze(0)\n",
    "    x1_values = x1_values.squeeze(0)\n",
    "    times_x0 = times_x0.squeeze()\n",
    "    times_x1 = times_x1.squeeze()\n",
    "    # print(f\"x0 squeezed {x0_values.shape}\")\n",
    "    # print(f\"cond squeezed {x0_classes.shape}\")\n",
    "    # print(f\"x1 squeezed {x1_values.shape}\")\n",
    "    # print(f\"t0 squeezed {times_x0.shape}\")\n",
    "    # print(f\"t1 squeezed {times_x1.shape}\")\n",
    "\n",
    "    total_pred.append(x0_values[0].unsqueeze(0))\n",
    "    len_path = x0_values.shape[0]\n",
    "    # assert len_path == x1_values.shape[0]\n",
    "\n",
    "    if model.memory > 0:\n",
    "        time_history = x0_classes[0][-(model.memory * model.dim):]\n",
    "\n",
    "    for i in range(len_path):\n",
    "        time_span = torch.linspace(times_x0[i], times_x1[i], 10).to(x0_values.device)\n",
    "\n",
    "        # print(\"-\"*10)\n",
    "        # print(f\"i {i}\")\n",
    "\n",
    "        if model.memory > 0:\n",
    "            # print(f\"cond memory>0 part 1 shape {x0_classes[i][:-(model.memory * model.dim)].unsqueeze(0).shape}\")\n",
    "            # print(f\"cond memory>0 part 1 {x0_classes[i][:-(model.memory * model.dim)].unsqueeze(0)}\")\n",
    "            # print(f\"cond memory>0 part 2 shape {time_history.unsqueeze(0).shape}\")\n",
    "            # print(f\"cond memory>0 part 2 {time_history.unsqueeze(0)}\")\n",
    "            new_x_classes = torch.cat([x0_classes[i][:-(model.memory * model.dim)].unsqueeze(0), time_history.unsqueeze(0)], dim=1)\n",
    "            # print(new_x_classes.shape)\n",
    "            # print(x0_classes[i].shape)\n",
    "        else:\n",
    "            new_x_classes = x0_classes[i].unsqueeze(0)\n",
    "            # print(f\"cond memory=0 {new_x_classes.shape}\")\n",
    "\n",
    "        with torch.no_grad():\n",
    "            if i == 0: # TODO: here is the issue to fix. Seems to be wrong for the paper figure generation\n",
    "                # testpt = torch.cat([x0_values[i].unsqueeze(0), new_x_classes], dim=1)\n",
    "                testpt = torch.cat([x0_values[i].unsqueeze(0), x0_classes[i].unsqueeze(0)], dim=1)\n",
    "                # print(f\"testpt at i==0 {testpt.shape}\")\n",
    "            else:\n",
    "                # testpt = torch.cat([pred_traj, new_x_classes], dim=1)\n",
    "                testpt = torch.cat([pred_traj, x0_classes[i].unsqueeze(0)], dim=1)\n",
    "                # print(f\"testpt at i!=0 {testpt.shape}\")\n",
    "                \n",
    "            traj = node.trajectory(testpt, t_span=time_span)\n",
    "            if noise_prediction:\n",
    "                noise_traj = node_noise.trajectory(testpt, t_span=time_span)\n",
    "\n",
    "        pred_traj = traj[-1, :, :model.dim]\n",
    "        total_pred.append(pred_traj)\n",
    "        if noise_prediction:\n",
    "            noise_traj = noise_traj[-1, :, :model.dim]\n",
    "            noise_pred.append(noise_traj)\n",
    "        \n",
    "\n",
    "        ground_truth_coords = x1_values[i]\n",
    "        mse_traj = model.loss_fn(pred_traj, ground_truth_coords).detach().cpu().numpy()\n",
    "        mse.append(mse_traj)\n",
    "        if noise_prediction:\n",
    "            uncertainty_traj = ground_truth_coords - pred_traj\n",
    "            noise_mse_traj = model.loss_fn(noise_traj, uncertainty_traj).detach().cpu().numpy()\n",
    "            noise_mse.append(noise_mse_traj)\n",
    "\n",
    "        \n",
    "        if model.memory > 0:\n",
    "            flattened_coords = pred_traj.flatten()\n",
    "            time_history = torch.cat([time_history[model.dim:].unsqueeze(0), flattened_coords.unsqueeze(0)], dim=1).squeeze()\n",
    "\n",
    "    mse_all = np.mean(mse)\n",
    "    total_pred_tensor = torch.stack(total_pred).squeeze(1)\n",
    "    if noise_prediction:\n",
    "        noise_pred = torch.stack(noise_pred).squeeze(1)\n",
    "    \n",
    "    return mse_all, total_pred_tensor, noise_mse, noise_pred\n",
    "\n",
    "\n",
    "def test_trajectory_sde(batch, model, noise_prediction):\n",
    "    if noise_prediction:\n",
    "        sde = SDE_func_solver(model.flow_model, noise=model.noise_model)\n",
    "    else:\n",
    "        sde = SDE(model.model, noise=0.1)\n",
    "    total_pred, noise_pred = [], []\n",
    "    mse, noise_mse = []\n",
    "\n",
    "    x0_values, x0_classes, x1_values, times_x0, times_x1 = batch\n",
    "    x0_values = x0_values.squeeze(0)\n",
    "    x0_classes = x0_classes.squeeze(0)\n",
    "    x1_values = x1_values.squeeze(0)\n",
    "    times_x0 = times_x0.squeeze()\n",
    "    times_x1 = times_x1.squeeze()\n",
    "\n",
    "    # if len(x0_classes.shape) == 1:\n",
    "    #     x0_classes = x0_classes.unsqueeze(1)\n",
    "\n",
    "    total_pred.append(x0_values[0].unsqueeze(0))\n",
    "    len_path = x0_values.shape[0]\n",
    "    assert len_path == x1_values.shape[0]\n",
    "\n",
    "    if model.memory > 0:\n",
    "        time_history = x0_classes[0][-(model.memory * model.dim):]\n",
    "\n",
    "    for i in range(len_path):\n",
    "        time_span = torch.linspace(times_x0[i], times_x1[i], 10).to(x0_values.device)\n",
    "\n",
    "        if model.memory > 0:\n",
    "            new_x_classes = torch.cat([x0_classes[i][:-(model.memory * model.dim)].unsqueeze(0), time_history.unsqueeze(0)], dim=1)\n",
    "        else:\n",
    "            new_x_classes = x0_classes[i]\n",
    "\n",
    "        with torch.no_grad():\n",
    "            if i == 0:\n",
    "                testpt = torch.cat([x0_values[i].unsqueeze(0), new_x_classes], dim=1)\n",
    "            else:\n",
    "                testpt = torch.cat([pred_traj, new_x_classes], dim=1)\n",
    "                \n",
    "            traj, noise_traj = _sde_solver(sde, testpt, time_span)\n",
    "\n",
    "        pred_traj = traj[-1, :, :model.dim]\n",
    "        noise_traj = noise_traj[-1, :, :model.dim]\n",
    "\n",
    "        total_pred.append(pred_traj)\n",
    "        if noise_prediction:\n",
    "            noise_pred.append(noise_traj)\n",
    "\n",
    "        ground_truth_coords = x1_values[i]\n",
    "        mse_traj = model.loss_fn(pred_traj, ground_truth_coords).detach().cpu().numpy()\n",
    "        mse.append(mse_traj)\n",
    "        noise_mse.append(mse_traj)\n",
    "\n",
    "        if model.memory > 0:\n",
    "            flattened_coords = pred_traj.flatten()\n",
    "            time_history = torch.cat([time_history[model.dim:].unsqueeze(0), flattened_coords.unsqueeze(0)], dim=1).squeeze()\n",
    "\n",
    "    mse_all = np.mean(mse)\n",
    "    noise_mse_all = np.mean(noise_mse)\n",
    "    total_pred_tensor = torch.stack(total_pred).squeeze(1)\n",
    "    if noise_prediction:\n",
    "        noise_pred_tensor = torch.stack(noise_pred).squeeze(1)\n",
    "    return mse_all, total_pred_tensor, noise_mse_all, noise_pred_tensor\n",
    "\n",
    "def _sde_solver(sde, initial_state, time_span):\n",
    "    dt = time_span[1] - time_span[0]\n",
    "    current_state = initial_state\n",
    "    trajectory = [current_state]\n",
    "    noise_trajectory = []\n",
    "\n",
    "    for t in time_span[1:]:\n",
    "        drift = sde.f(t, current_state)\n",
    "        diffusion = sde.g(t, current_state)\n",
    "        noise = torch.randn_like(current_state) * torch.sqrt(dt)\n",
    "        current_state = current_state + drift * dt + diffusion * noise\n",
    "        trajectory.append(current_state)\n",
    "        noise_trajectory.append(diffusion * noise)\n",
    "\n",
    "    return torch.stack(trajectory), torch.stack(noise_trajectory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader_1d_3m = eICUDataLoader(batch_size=512, file_path=file_path, \n",
    "                             t_headings=['t'],\n",
    "                             x_headings=['x'],\n",
    "                             cond_headings=[],\n",
    "                             memory=3)\n",
    "\n",
    "train_loader_1d_3m = data_loader_1d_3m.get_train_loader(shuffle=True)\n",
    "val_loader_1d_3m = data_loader_1d_3m.get_val_loader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100/1000, Loss: 0.0017163181910291314\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yp325/.local/share/virtualenvs/clinical_trajectory-JXbBiG1u/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/yp325/.local/share/virtualenvs/clinical_trajectory-JXbBiG1u/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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37rh37x5mz56NGTNmKJ1bbpg2bRomTJiA27dvo2LFiujRowdSU1MBAFevXsWAAQOUta8/+eQT/PDDD1me79q1awCAf//9F8HBwdi7d6/y2MmTJxEQEIATJ07gn3/+yZF9WZ3v9OnTePr0KU6fPo3Nmzdj06ZNefoMcoVg8k1UVJQAIKKiojR74REjhACE6NJFyOVy0XROaYHZEOjbQgBCeJaUiffvqeuZM0J4eAhx9apmTWQYQyQhIUE8fPhQJCQkpD8YG5v59nH/rPrGx+esby6Ijo4WZmZmYvfu3cq2yMhIYW1tLUaPHq1sK1WqlOjSpYvaa3v27Ck+/fRTtbaJEyeKqlWrKp8DEPv27VPrY29vLzZu3CiEECIoKEgAEOvWrVMef/DggQAg/P39hRBC9OjRQ3To0EHtHN26dRP29vaZvi/FeW/duqXW3rdvX+Hq6iqSkpLU2nNqZ0bnK1WqlEhNTVW2ff3116Jbt24Z2pXV9yQ3foNH1PqMohj6wYOQvH6NFf12YaC/DZztbwCOT/DqpRTffQcIASxeDLx9S6Iod+9q12yGMWiKFMl8+/JL9b4uLpn3bd9evW/p0hn3ywXPnj1DSkoKGjZsqGyzt7dXTgmnpX79+mrP/f390aRJE7W2Jk2a4MmTJ5DJZLmyo2bNmsrH7u7uAICwsDDldby8vNT6e3t75+r8aalRowbMzc3z/PqPqVatGkxMTJTP3d3dlbYXFuyo9ZmqVYEWLUiKbO1a1CjZAGva/Y5VJ+KAr7oDJsnYtw9YuRLYvh3w9gY+fKCKWwEB2jaeYRhdxsbGJtevkUgkoAGriozWb83MzNReAwDyQpJUzOh95NTOjEhru+JchWW7AnbU+s7o0UC/foAit693b3xhWh1t424CHQcCAMaNE3j6FDh8GKhdGwgLo1rWz55py2iGMWBiYzPf/vpLvW9YWOZ9jxxR7/v8ecb9ckHZsmVhZmaG69evK9uioqLw+PHjbF9bpUoVXLx4Ua3t4sWLqFixonKE6ezsjODgYOXxJ0+eID4+Plc2VqlSBVevXlVru3LlSpavUYyYczqyz87O3J6vsGEJUX3niy9oU2BigsjZUxF1uBdQfgvwvCWS7vRHt27AjRvA8eM0CPf3B1q1As6dA0qW1J75DGNw5GYkWlh9M8HW1hZ9+/bFxIkT4ejoCBcXF8yaNQtSqVQ5ss2M8ePHo0GDBpg3bx66deuGy5cv4/fff8eKFSuUfVq1aoXff/8d3t7ekMlkmDx5croRaHaMGjUKTZo0weLFi9G5c2ccO3YMR48ezfI1Li4usLKywtGjR1GiRAlYWlrCPot0l+zszO35ChseURsgDp27wcTKGpAA6DQEJvYhCAgARo4EnJ2BkyeBChWAFy+AhQu1bS3DMJrkl19+gbe3Nzp27AgfHx80adIEVapUgaWlZZavq1u3Lnbt2oUdO3agevXqmDlzJubOnYt+/fop+/z888/w9PREs2bN0LNnT0yYMAHW1ta5sq9Ro0ZYu3YtlixZglq1auH48eOYPn16lq8xNTXF0qVLsXr1anh4eKBz585Z9s/Oztyer7CRiI8n6plcEx0dDXt7e0RFRcHOzk47Rly7BmzZAkyeDHh64ua6eaj/eiaEBEBwLUjW3oSQS/HHH8C33wKvXwM//URBZgUYZ8EwRkFiYiKCgoJQpkyZbB2crhMXF4fixYvj559/xoABA7RtjkGR1fckN36DR9SGwsSJwPLlpFgGoG7fKRj4+L+IUPc7QIu5AChQ/MkToEQJYOlSlZMWghXMGMYYuHXrFrZv346nT5/i5s2b6NWrFwBofdTIZA47akOhd2/a//EHeV0zM/zgPQ0OCdQsms1FsSr3EBsLdO8OJCWpXioETYs3bQq8e6d50xmG0SwKMRMfHx/ExcXh/PnzcHJy0rZZTCawozYUvvoKsLAAHjwA/hOSdx4wCnNu/BeAIhV4374N7Iom4+ZNYNIk1UtDQ4F9+4D79ykanJ01wxguderUgZ+fH2JjYxEREYETJ06gRo0a2jaLyQK9c9TLly9H6dKlYWlpCS8vL6XUW0a0bNkSEokk3fbZZ58p+/Tr1y/d8Xbt2mnirRQsDg5Ap070+I8/aG9tjaGfTEa1MMAxyQSwC4FD9zEAaNr74EHq5uYGnDoFuLsD9+6Rsw4P1/g7YBiGYTJArxz1zp07MW7cOMyaNQs3b95ErVq10LZt20xVYfbu3Yvg4GDldv/+fZiYmODrr79W69euXTu1ftu3b9fE2yl4FNPf27YB/+nmmg0ficP7bfDoNxkcTWzx0nUlWvW4A4DSr1++pJdUqkTO2s2NnHWrVpTiyTAMw2gXvXLUv/zyCwYOHIj+/fujatWqWLVqFaytrbFhw4YM+zs6OsLNzU25nThxAtbW1ukctYWFhVq/okWLauLtFDzt2gHFitFc9r//UpuDA0p2HwznBGDuU08AwO3q7VCnbio+fAB69AAUgjyVK1O1LYWz/uQTICRES++FYfQATpphsqKgvh9646iTk5Ph5+cHHx8fZZtUKoWPj0+6EmmZsX79enTv3j2dpNyZM2fg4uKCSpUqYejQodmWVNNZzM0pUszDA4iMVLX/pwneb+tDuFgWQ0RKCGqPXAA7O+DSJWDWLFXXypWBM2foFAEBJJLCMIw6CnGM3KpuMcaF4vuRW9GXj9EbZbLw8HDIZDK4urqqtbu6uuLRo0fZvv7atWu4f/8+1q9fr9berl07dO3aFWXKlMHTp0/x/fffo3379rh8+bKa8HpakpKSkJQmbDo6OjoP76iQmD8fWLIESGt7+fJA27b4PfYYwhLpJmTLy9n4+df/YcwAD/j6klpZ27bUvVIl4OxZKt7RsaPm3wLD6DomJiZwcHBQLrtZW1tnq+zFGA9CCMTHxyMsLAwODg6Z+pKcojeOOr+sX78eNWrUUKsaAwDdu3dXPq5RowZq1qyJcuXK4cyZM2jdunWG5/L19cWcOXMK1d48k1ni/PDhGPbVMSzxliC4iIBMyHDWZgSGDNmLVatoefv2bRpJA+Tby5dXvfzFC0AmA8qWLfR3wDB6gZubGwAUeuUkRn9xcHBQfk/yg944aicnJ5iYmCA0NFStPTQ0NNsPIi4uDjt27MDcuXOzvU7ZsmXh5OSEwMDATB311KlTMW7cOOXz6OhoeHp65uBdaJDUVErVqlWLnnfoAFv3Ulhw4gX6/icNvu/RPhwfcwGXLzfFnTtAz560tG360bfi7VuKBE9IIPnRypU1+1YYRheRSCRwd3eHi4tLjisvMcaDmZlZvkfSCvTGUZubm6NevXo4efIkuvxXKUoul+PkyZMYMWJElq/dvXs3kpKS8O2332Z7ndevX+P9+/fKGqkZYWFhAQsLi1zZr1HCw4EqVUhqLDQUKFqUpsKHDsW3U6dgeUtrXCtKayffnxuLHTuvokF9Kc6eBebMAebNUz+dREIp2k+fAs2bAydOqPw/wxg7JiYmBfaDzDAZoTfBZAAwbtw4rF27Fps3b4a/vz+GDh2KuLg49O/fHwDQp08fTJ06Nd3r1q9fjy5duqBYsWJq7bGxsZg4cSKuXLmC58+f4+TJk+jcuTPKly+PtooFW33EyYlCt1NSgL//VrUPGACpuQWW/KUKgLnx9gZuJu/AmjX0/McfqcJWWtzdac26Th0SQ2nZEshh/B7DMAyTT/TKUXfr1g2LFy/GzJkzUbt2bdy+fRtHjx5VBpi9fPlSrcYoAAQEBODChQsZis2bmJjg7t27+Pzzz1GxYkUMGDAA9erVw/nz53V7xJwTvvqK9rt3q9qcnIBu3dDoNdA7RrXYPPXkVHT5KgGDB5OcaK9ewJs36qdzcqI868aNKaDcx4dG1gzDMEzhwtWzCgCdqJ71MQ8eANWrU8pWWBigqKV67Rrg5YU3xcwx/JfWuBJ2E6FxofBt7Ysx9aegUSPgzh3A25tyqj++X4mLA7p2pVG3mRkN2PV58oFhGEYbcPUsBqhalaK+kpOBf/5RtTdoANSujeLvk7E/5jMs+nQRAMD3gi9i5eHYs4fUSC9fBoYMoRF2WmxsSHr066+BMmVoOpxhGIYpPNhRGyoSiWr6e88e9fb/1vSxaRN61eyF2m61EZ0UjR/P/Yjy5YFduwCpFNi0Cfj11/SntrAAtm8Hzp8HXFwK/Z0wDMMYNeyoDRmFoz5yBIiJUbX37Enz1jduIOXubXjYUvL079d+R9CHIHz6KfDLL9R14kTg6NH0pzYxUXfS69ZRqUyZrJDeC8MwjJHCjtqQqVmTlMouXgSKFFG1OzkpJcfM/9iKxJREAECqSMWM0zMAAKNGAQMGAHI5qZL6+2d+mefPSaX0999JOzxtrWuGYRgmf7CjNmQkEmDqVKBePXqcln79qMufW7HEZzFMJJQHuvXeVtwKvgWJBFi+HGjalNKxO3SglOyMKF2aKmuamVGQebt26lLjDMMwTN5hR22stG8PODsDoaGofusNhjUYpjw06d9JAGgteu9eoFw5GjV36kRR3xnRvTtw+DBga0tFPZo2VZXQZBiGYfIOO2pj4OxZ4H//I0+qwMwMUCi1bdqEOS3noKgllff899m/+PcZlcl0dqYl7mLFgOvXaXk7s3VoHx8KMPPwoOwwb2/SD2cYhmHyDjtqY+DgQWDjRgrnTst/0984eBBF4+Xwbe2rPDT+2HjIhRwAUKECncLCgvZjxqRP21JQqxZw5QpQrRpphH+scsYwDMPkDnbUxsDnn9P+n3/Uh8M1a1IidEoKsH07vqv7Haq7VAcA3A27i90PVKpmjRvTOjRAQWPTpmXurD09gQsXqNrmxImF8YYYhmGMB3bUxkCTJlSY4/379CLdilH1pk0wkZpg11e7MLUp6aV/f+p7JMuSlV2//hpYupQe+/oC33+fubN2cKDIcUUMW0wMsGgRp28xDMPkFnbUxoCpKYVtA+pFOgDKpzIxAfz8gGfPUMW5CqY1mwZXG1c8+/AMa/zWqHUfOZJGygCwYAEFlWcnQisErW1PmkTyo7GxBfS+GIZhjAB21MZCp060P3hQvd3ZmcphAcoCHjbmNpjVYhYAYNrJaYhJilF7yahRqpH1woXA+PFZj5QlEopbU6xxN2kCvHiR3zfEMAxjHLCjNhbataOR9aNHwJMn6se++Yb2aYLN7C2piEd0cjR+OPdDutONHAksW0aPf/2Vsr3CwzO/fLdulLbl6grcvUuS4xcv5ucNMQzDGAfsqI0Fe3saOVepAoSEqB/74gua/r55E3j2DADwddWvUdy2OADglyu/IDQ2vdrJiBHA1q2AtTWVvKxbl4pzZUajRnS8dm2qa/3JJxSMzjAMw2QOO2pjYv9+4OFDoFkz9fYMpr/NTMywqfMmAECqPBVjjo7J8JQ9e1I6VvnywKtXdOpffgFSUzM2oWRJigj/8ksKNp8xQ12GnGE0TnAwsHYtMHAgpTOkpWZNoFQp+sLu3g0kJGjHRsaoYUdtTNjYZH7s669pn2b626ecD5qXag4A2PlgJ55GPM3wpTVqADduAF26UFXN8eOB+vXJgWdmxq5dwJw5pHxma5uXN8Mw+eT1a5oWKlMGGDSIKst8rDXw8iVte/fSEpGLC9C7N63fMIyGkAiRXcwukx25KQCuEyQmAh8+AO7uqrZ37wA3N6rC8fQpULYsAOB19GuU/q00ZEIG7xLeuDTgUqanFYJ+6yZPptNLJDRI+eEHGrRnx759pBvONa6ZQuXdO2DmTGDDBrqzBChowscHaNgQ6NxZlVcYGEhLRYcOUW1XRRSkiQkVvJk0STvvgdF7cuM3eERtbGzdSnqgY8aotzs706IxoJz+BoASdiUwouEIAMDl15dx7XXmi9AKxxwQAPTtS457zRoasHz/PRARkblZt27RNHrjxsDmzXl9cwyTAyZOBFatIifdvDlw8iRw9So53i5d1AvYlC9PwvW+vkBQEHDpEk2Dy2RApUpaewuMccGO2tgoXx6IjycB7+Rk9WOK6e80jhoAFn26SFmz2veiL7LD2RnYtIkkxuvXp0Ievr40Wp41K+PKWqVL031CYiJpsAwbxuUymUJi8WLg00+B06fpS9qqVfrqchkhkZCA/Z49FBXZubPq2NOnrObDFBo89V0A6NXUt1xOVTNCQ2kk0aqV6lhYGE2HfzT9DQD+7/xRfWV1yIUcVwZcgVcJrxxdTgjSWJk5E7hzh9ocHICxY4HRoykYPa1p8+bR2rUQgJcX/SaWKFEA75sxXhITgR07VCp8uSE1lf5P7tyhafDAQFqztren/yN3dwqy2LgRaNFClQbBMNmQK78hmHwTFRUlAIioqChtm5Iz+vQRAhBiwoT0x1q3pmMLFqQ71G9/P4HZEE3WNxEpspRcXVImE2L3biGqVaPTA0I4OAgxZ44QHz6o9z10iI4BQjg5CXHsWK4uxTAqkpOF6NyZvkwbNuT8dcHBQsybJ0SJEqovbE62MmWEePKk0N4OYzjkxm/w1Lcx0r497Y8eTX/sq69o/7GCGYDZLWbDRGKCi68uYtihYemOZ4VUSqe+exfYuROoWpWmwGfNUk2JK9awO3QgRdM6dUhEhYVRmDwhk9Eo+sABksXLydRMTAwFWpQsSbmDr19TTEePHjQttGULTZf/8w8FYMyeDXz2GX3BAVrHrlSJVIB4spIpKDRw42Dw6N2I+v17IaRSGgG8fKl+7MULapdKhQgPT/dSn80+ArMhpLOlIvB9YJ5NSE0VYvt29RG2ra0Q33+vumxCghC//UZ9GSZXyOVCDBpEXyxTUyH++Sf719y+LUTFiqovpLe3EH/8QV/E7Hj7VoiJE4UwM1O9vn17IUJC8v9eGIMkN36DHXUBoHeOWgj6EQKEWLMm/bHq1enYtm3pDr2LfSdM5pgIzIaouaKmkMvl+TJDJhNizx4hatZU/b4VKSLE5MlChIWp901IEKJtWyGOH8/XJRljYMIE1Q3nzp1Z95XLhVi5UggLC3pNiRJCnDmTt+s+eUJ3nIovs6OjEPv25e1cjEHDU99M9gwbRuWv0gaTKVBU2jp8ON0hJxsnjGk0BgDVrP7z7p/5MkMqpWyXW7coj7pOHaqutXAhpXWNGQM8f059f/4ZOHYMaNuW0r0yUz9jjJw//qDIboAUxxRa9hkhl5PYydChlGbQsSNw+zYFhuWF8uVpatzaGrCyovWcL74ge3gqnMkrGrhxMHj0ckSdFWfOqCK5ZLJ0hxNSEoS9r73AbIgi84uIiPiIAru0XC7EwYNC1KunGpRIpUJ8840Q584JMXiwqr1xYyGCggrs0oyhMGcOfUFmzcq6n1wuxMiR1NfERIjFi6mtILh2TYiICCFGjFB9YUeO5HUcRglPfWsYg3PUyclC2NnRj8vVqxl2We+3XmA2BGZD9Nnbp8BNkMsp2rtNG/Wg2nbthJg/X2WenR2tdTOMGhcvZu8Up01TfbH++KNw7JDLhfj5Z9V1unYVIj6+cK7F6BU89c3kjIgIyvvcuVO93cwMaNOGHmcw/Q0A/er0Q3nH8gCAB+8eQBTwtJ5EQiYcO0YprH36UJXOo0dp2rtePaqXEB1NAbk//VSgl2f0kbRrIY0bk8xnZixcCPz4Iz1esYIKpheWTWFhJHBvZkaa4W3bAlFRhXM9xiBhR23MHD1KP1CKH6y0ZLFODQBSiRQrP1sJALgTegf3w+4XlpWoWZNkRQMCKNtGKiVRKUVda0dHoHv3Qrs8ow9s3gw0aaIKaMiu75Qp9Pinn2h9ujA5e5bk+cqXB+zsgPPngdatgffvC/e6jMHAjtqYadOGhq737lG+aFrataP99eukYpYBPmV98EXlL5AqT8XgfwZDJi9cCcWyZUkA6tEjGkUrzLOwoFLaCg4eTK+Oyhgwb94AI0eSrOfHs0Mf8+gRBVICwNSppPtdmJiZUTEPBwfA35+0xJ2cSCigRQsqsckw2cCO2phxcqJqQQDNMafF3R2oWzfjY2lY1n4ZbM1tcfn1ZVRcVhFXX18tJGNVVKgAbNsG/PsvUK4c/dZ98QUJqmzfThLMjRoBDx4UuimMLjByJAmVeHtn7XiTkugOLz6eKmX98INm7Ctdmip1ASSY8sMPJD/64AEVcFdU5GKYTNA7R718+XKULl0alpaW8PLywrVrmVdz2rRpEyQSidpmaWmp1kcIgZkzZ8Ld3R1WVlbw8fHBkydPCvtt6A4KlbIjR9Ify2b6GwCK2xXHj61o6vxZ5DP02d8HSamaqabRujVNBkydSuvXf/0FDBkCFClC6V5169LMJtdKMGAOHKC8PlNTUgqTZvGTNmUKpV45OZHDzKpvQfPFF1T7GiAx+0OHKP/w6VOgZUvSD2eYzCj00LYCZMeOHcLc3Fxs2LBBPHjwQAwcOFA4ODiI0NDQDPtv3LhR2NnZieDgYOUW8pFS0IIFC4S9vb3Yv3+/uHPnjvj8889FmTJlREJO1Ij+Q6+jvq9epWhUe3shUj7S7754USXK/fGxNKTKUkXtVbWVUeDTT04vXJsz4NYtIWrUUAXXenioC0wFBGjcJKawiY5WaXFPnZp130OHVF+Iv//WjH0fk5AgRIUKZMPgwUK8eiVE+fIqjfCPVQIZg8Zg07MaNmwohg8frnwuk8mEh4eH8PX1zbD/xo0bhb29fabnk8vlws3NTSxatEjZFhkZKSwsLMT2XOT86LWjTk0VomhR+rG4dCn9MUdHOnb+fJanufn2ppDMlpC86BypuPb6WiEanTGJiSRBqlBHtbERwtKSHltZCbFuncZNYgqTUaPoj1uuXNYpT6GhQjg7U99RozRnX0acPk03vmvX0vNXr8h+QIiyZek5YxQYZHpWcnIy/Pz84OPjo2yTSqXw8fHB5cuXM31dbGwsSpUqBU9PT3Tu3BkP0ixcBgUFISQkRO2c9vb28PLyyvKcBoWJCc0hA+oRWYpjijSt48ezPE0d9zoY22gsAEAu5Ph277dISEkoaGuzxMKCAtgvXQKqVaNA28REyoxJSKB4HsZASE6m6GkAWLmSVMAy4/vvgXfvgBo1KC1Lm7RsSWvS331Hz0uUoBSGsmWBZ8/o+Lt32rSQ0UH0xlGHh4dDJpPB1dVVrd3V1RUhISEZvqZSpUrYsGEDDhw4gD///BNyuRyNGzfG6/8inBWvy805ASApKQnR0dFqm17j60sRWcOHpz+mcOKnT2d7mrmfzEUFxwoAgMcRjzH91PSCtDLHeHnRUuTy5ZS6FRdH7cuXA2fO0PzngwdASopWzGMKAnNz4OpVCvH/9NPM+926pQrkWrUK+ChGRSukrT0skwGenvT/pViz7tOHpE0Z5j/0xlHnBW9vb/Tp0we1a9dGixYtsHfvXjg7O2P16tX5Oq+vry/s7e2Vm6enZwFZrCXKlwfc3DI+9skntL96VeXxMsHG3AZ7vtkDU6kpAGCv/14ky7STJ2VqSlk4T54Ao0ZRlszp0/R2GjUiPYwGDYAbN7RiHlMQmJkBnTplflwIYOxY2vfoQX90XeLQISqJef8+ldU8eJBuJI4eJWF7hvkPvXHUTk5OMDExQehHOb2hoaFwy8zJfISZmRnq1KmDwMBAAFC+LrfnnDp1KqKiopTbq1evcvNW9IuyZemOPyUlR4Wha7rWxJJ2SwAAr6Nf427o3cK2MEscHYElS4DAQHLc5uaUbhsdTYpnDRtSdk829yCMrpCSQtHdSTnILNi3j8RGLC2pAI2usW4djaBHjaKbierVgaVL6dj33wPGsvzGZIveOGpzc3PUq1cPJ0+eVLbJ5XKcPHkS3t7eOTqHTCbDvXv34O7uDgAoU6YM3Nzc1M4ZHR2Nq1evZnlOCwsL2NnZqW16z4kTNIU4YYJ6u0SiqrCVg+lvABhafygJoYhUdN/THdFJ2l8aKFmSpr6fPQNGj6biRgD9Pv7+O6W1/vYbFzjSedavBwYPBpo3z/qPlZio+i5PnEhfAF3j118psOL0aRpNA7R23a0bSY927w58+KBdGxndQAPBbQXGjh07hIWFhdi0aZN4+PChGDRokHBwcFCmXPXu3VtMmTJF2X/OnDni2LFj4unTp8LPz090795dWFpaigcPHij7LFiwQDg4OIgDBw6Iu3fvis6dOxtXepaCvXsp8rRixfTHNm2iY15eOT5dRHyEKPlrSYHZEOWWlBN/PfyrAI3NP5GRQvz6qxBubupFP4oUEWLmTCorzOgYMTFCuLrSH2rZsqz7LligytOLidGMfXlh6lSys3x5IZKSqC0qShUJ3rVrwVX0YnQKg03PEkKIZcuWiZIlSwpzc3PRsGFDceXKFeWxFi1aiL59+yqfjxkzRtnX1dVVdOjQQdy8eVPtfHK5XMyYMUO4uroKCwsL0bp1axGQy6Rbg3DUHz5QqT9AiBcv1I+9eKEqBZiL93j51WVhMsdEYDaE1Q9W4k30m4K1uQBITRVi924hKlVSd9iK/OuVK4V4/17bVjJCCFX5ynLlVE4tI8LDhbC1pb5btmjOvrwQHa26+fjlF1X7jRtCmJlR+9692rOPKTQM2lHrIgbhqIUgzwRknHCsuMP/559cnXLTrU1KIZRqy6sJmTx9fWtd4dIlcsxt26pysQEhzM2F6N6dynTz4EZLhIbSdAcgxI4dWfedNYv61a6dYT11nWPdOpXo0Lt3qvbp06nd01OI2FitmccUDgaZR81oAEWaS0Y504ro7xyuUyvoW7svhtQbAoDKYY46Mio/FhYq3t4kQXr0KPDqFVCxIrUnJwM7dlCKa9WqFJwWH69VU42Pn34CYmOB+vWBr7/OvF9MjCoga9o0zcqE5pV+/YDatan05a5dqvapU0kn/NUrYN48LRnH6AJ68C1mNIZC3OTkyfR5nApHfepUrk+7/LPlqOtOBT6WX1+OvwP+zo+VGqFYMYqhk0jouZkZbY8eAWPGUEbb6tWci60R3r+nHGiAdLKzcr6rV1MAVqVKpK+tD5iYkGjL0aOqyl4ARTwqbjp+/pmqbzFGiUQIjnPNL9HR0bC3t0dUVJR+R4CnpJCHiomhBON69VTHgoMpNFoiAcLDKe8pF8Qlx6HEryUQmRgJcxNzBI0OgoetRwG/gYLn+nVK37r6X1EwZ2f6CMLC6HmFCqQX07WryqkzBUxQEKUwvX1L38vMPujERBINCQmheqj9+mnUzELj88+Bv/+mm+WTJ/mLZiDkxm/wiJpRYWYGdOxItag/Ljnl7g5UrkzLtufO5frUNuY2ON//PKQSKZJlyej1Vy/owz1igwYkSbp5M2nCvHtHTrpfP3LaT55Qec2OHbkAUqFRpgw5qvPns3ZSmzaRk/b0BHr21Jh5BU54uHrpyyVLKBf89Glag2GMDnbUjDrbtlHJS0Wd6rTkY/obAKq7VMe6TutgJjXDmRdn8OP5H/NhqOaQSknVMSCAUnKrV6cZ1qdPgRkz6P7m8GHV+jWX1SwkFMnvGZGaqtLxnjiRlG30kb/+IpEhRUlMgG5Upk2jxxMmcICEEcKOmsk5uRQ+yYj+dfpj5WcrAQAzTs/Afv/9BWCYZrCzo5immzfJD9jakqOuXBkoVYrUzcaMIYnSj+ubMHkgPh6YPBn4T5s/S3bsAJ4/p2mOAQMK3bRCo0YN+iL98w9w5YqqfcIE+pK9fUt3g4xRwY6ayZg3b2hdOi0tW9L+/n3VIm0eGFB3AEY0oBHDl7u/xPU31/N8Lm1gZqZ6vGMHcO8ezVQWLUozlDdu0JT5mDEkVcrkkbVr6c7IxydrFTIhVKPpsWOzHnnrOhUrAn370uMZM1TtlpZUGg6goAiusGVUsKNm0jNxIpXf+/139XYnJ7rjB1QlBvOIb2tfWJhYQC7kaLWlFSITIvN1Pm3RqxewZQsti374QPFMDg4UNL9kCVClCi2dcnR4LklKAhYtosdjx2a9Nn3+PN08WlsDQ4dqxr7CZOZMuhv8918q96agRw+gTh0K9vzhB62Zx2gedtRMeqpVo30aDXQlzZvT/uzZfF2iiEUR7Ou+DwAQmxyLxhsaQy70r7SfVAr07k3r1wsW0PR4ZCQds7Skmcr+/SlbaN06yslmcsDOnTSr4+GRffT2ihW0//Zbwyg6Xro0MHAgPZ4+XTWbIJXSDANA6VxPn2rFPEbzsKNm0qNYi75+nUQY0qJw1HmI/P6Y9uXbY0qTKQAA/3B//O/A//J9Tm1hZUXLqc+eAePHU62FJk1oRtbZmTKMBg6kdK6ffqLAXiYThKAKKQAFVVlYZN43OJgCsADDGE0rmDaN7vQuXlSPCfHxAdq2pSkaRYAZY/Cwo2bSU7IkKXrI5ekdssJR371bIJV95reej6aeTQEAm+9sxoZbG/J9Tm1SrBiweDHw+DENeiZNohinOXPod/flS3LoxYvTSPzSJa7YlY4LF4Bbt+gDGzQo677r1lHEd+PGpO5lKHh4UCUtU1Oqx5qWhQtpKWDnTqrZyhg87KiZjGndmvYfT3+7uVHAixA5qk+dHRKJBMd7H4eztTMAYODfA3E7+Ha+z6ttSpak0TNAS6eRkbR+LZWSM09OBv78k0bdlStTfFBOgpuNAkVUc+/e9GFlRmoq5ckBwPDhhW+Xppk2jaa3x45Vb69Vi/IFAZIZZQwedtRMxmTmqIECW6dWYGVmhYv/uwhTqSnkQo6v93yNyMTIAjm3rtCnD/DZZzRJ8f49qUZWqECDxsePge+/J+fevj1w6FB6BVejQQhKQ7K1JTWyrDh4kNaxnZ2BL7/UjH2axM0t8zrac+ZQjuCpUxR0xhg07KiZjFGIm9y/D4SGqh9r0YL2BbBOraBCsQq49t01lLAtgcCIQPTa2wsyueEoh9SuTamxly/TEqNMRqpmqakUEtCiBfmoo0dJ5axiReDXX9OHCBg8EgnpWoeEkLJMViiCyL77Lut1bEPg3j0gMFD1vFQpqiAD0F0er58YNOyomYxxcgJmzQK2bwdsbNSPKUbUfn5U0aiAqONeBwd6HIClqSUOPzmMgQcHFti5dYVGjcgZX7xIxcpSU0nR7MwZ+h0eP54Cl58+BcaNowDgefOM0GFnlwv96JFK93rwYM3YpC0WLwZq1lTPqwZoatzGhoI+9+3Tjm2MRmBHzWTO7NlA9+5AkSLq7SVL0h29TEbRUAVIXfe6WNGBRkob72zEj+f0Q2Y0tzRuTNVEL10CplDgO8qVowqOLVrQ0mPlyrS2PXOmymEbdLT4sWM0S5OT0aFibbpjR/ouGjI+PrTftYumYRS4uKjWr6dPZ+1aA4YdNZM3CjBN62N61+qNMg5lAADTT0/Hnod7CvwauoK3N0WAK5g/HzhwgILLSpZUSZQqHHbx4qR7cfq0gc12CgGMHk13KX/+mXVfRSQeYPijaYDWTRQBDgoFNgUTJlAlO3//7D83Rm9hR81kzfXr5DVevVJvL4R1agWmUlP4DfKDnQWVfuu+pzv83voV+HV0kR9/JLUzqZRG3PPmUVzV2LFA3brko3bsoHXt8uVJRO7CBQMYTJ05Q6oxRYoAnTtn3ffIEZpacHOjBX9jQJEz/ccftH6vwN5eNSUzaxYpujEGBztqJmvGjqVglWPH1NsVI+qrVynvqIApalUU1wdeh5nUDDIhQ7ONzfA2+m2BX0fXqF6dBkZPnlCskKUl3Sv9+isFN/v5UbutLYmrLF4MNGtGVUh79KAB15EjpIimVyPuNWto37MnybtlxebNtO/Vi/KMjQFvb1ovSU5OL+07fDh9AV68UH2OjEEhEfpQFFjHyU0BcL1j5kwa1vXsCWzdqmoXgkQZQkJoNKQYYRcwp4JOwWeLDwQEnK2d8WLMC1iZWRXKtXSRsDBg2TJg+XJg7lxV9cOICBLkOnuW0rkUsqVpMTcn567YihShGC3FZmFBNwIWFjR7WqUKBbY5O2v0LVKBiRIlyAn5+dHUQWa8f09OKSWFRHcU2vPGwL59QNeuVP3l1Sv1IM9Vq0iZzdWVIhE/DgBldI5c+Q3B5JuoqCgBQERFRWnblILn1CkhACHc3YWQy9WPdetGx+bOLVQTVt9YLTAbArMhvtn9TaFeS1eJiREiPl71fONGIczNhejbV4irV+nPNH8+/UmqVBFCKqU/TV42JychOnUSYssWISIjNfDmFi2iC9evn33fZcuob506hW+XrpGaKkS5ckI4Owtx7Zr6seRkIcqWpc/mxx+1Yx+TK3LjN3hEXQAY9IhaUQ4qKYkCVipXVh1bsYKm3Xx8gBMnCtWMyScm46dLVJBgw+cb0L9O/0K9nq4zYACwIY3aarNmFIvVuTPNBicmUvr7u3eqLT5etcXF0Z80KYn6hoTQnzcoSP065ua0DNyzJ53bqqAnM4SgiiVPntC07cBsUvIaNKA6okuWZC+IYoj4+wNlytBUyMds3UqFSezt6Q9ZtKjm7WNyDI+oNYxBj6iFEOKTT+hOfcUK9fZ796jd2lqIpKRCN2PumbkCsyEs5lmIM0FnCv16us6VK0L06CGEqalqNFyiBI2sP578yCmxsTRYmzmTRuZpR9p2dkJ8950Q587R4K5AePWKRoJFitC0QVbcv0+GmJoKERZWQAYYEKmpQlSvTp/RlCnatobJhtz4DQ4mY7JHoVKWtooPQAuaxYrREM2v8KOypzWfhk4VOyFJloRWW1ph1Y1VhX5NXcbLC9i2jYp+fP89adS8fk0hA1mVb84KGxsatM6ZAzx4QIJY06ZRqlh0NNXAaN6c1rG/+YaeP3uWj8C1EiVoNH39evp8/Y9RBJF99pkWFtJ1DLk8/f+jiQmlDQA045A2OpzRa9hRM9mjcNTXr6v/IkulqujvtAXuCwmpRIotX2yBg6UD5EKOYYeGYfeD3YV+XV2neHH6fX71inxZ2uqHb96Q4129GoiJyd15JRKKQv/hB5pJPX2aSkPb2VHhtN27aaa6XDm6X2vVipTVNm8GrlzJRXE1qVR9SSUjUlNVecJ9++bujRgaqamUW92qVXrBoU6dSP4uIYH+cIxBwGvUBYBBr1EDFI17/Tr94pubqx9bupQWR9u2JW1MDXA39C7qrq4LmZBBKpFiz9d78EWVLzRybX1jzhwSmANowNqzJ1WOrFcv7+dMTaXqiidOUNbejRsUhJ0Rzs40aHZyUkWfu7rSVjYlAI51S6NKbQuYmWVz0WPHgHbt6I7g7dv030NjQxGk8NVXdMeUltOnyYmbmVHFl9KltWIikzW58RvsqAsAg3fUWXHnDt3dFylCOUPZ/uIWDNvubUOvvb0AACYSE+ztthefV/pcI9fWJyIigE2bKHsnrfpk3bpUy6Jv3+xltbMjKYmmyW/dou3hQ9IueZtl2rvAQ1SFE8LR1fwQ5PUbomFD8i8dOtAsrhp9+pDYx/Dh6fOIjZF790j/28SEpjs8PdWPt25NlbUGDVLJrTI6BTtqDWPUjloup+HShw803+nlpbFLjz4yGkuvLQUAmEnNsLfbXnSs2FFj19cnhKCc6zVrKP86OZkcdHBw9voieSU2lm4OQkIo6jw8nPLCw8IA+0dX8evlRoiDNdwQgljYKl9XpgwwZgzQvz8JuyA+nobgsbE01evtXTgG6xutWtHoecoUUg9My/nztCxlakrVXgxdD10P4ahvDWPwUd9CCPHhgxBDhwrRqJEQMpn6sc6dKdJ04UKNmpScmiyabWimzLFus6WNkOc13NmIePdOiF9/FWLOHPX2zp2FmD1biKAgDRgxZIgQgJB/21sEBFDO9tChQjg6qqLM7e2FmDxZiLA1+6ihTJm8h7MbIvv30+fi6KieZK+gdWs6PmiQ5m1jsiU3foMddQFgFI46OZlSaAAhbt1SP/brr9Tevr3GzQqOCRbui90FZkN02dFFyOSy7F/EpOPWLfVUrJYthVi/XohC+UonJJAXBoQ4eVLtUFycECtXClGxosoWG5N4MRm+ImwMC3mokZoqROnS9CGtW5f++PnzqnQ2jdx9MbmB07OYgsfMjFQ1AFr7SotCPvTCBYo00iBuRdyw55s9MJOaYf+j/Zjy7xQIIXDpVcGW3zR0KlakoGofH4r2PnOG4pVcXUlD/ObNArzYgQNUYLtkSaBlS7VD1takZe7vD+zfD9StlYo4mRUWYgrKrJ6CCROyW/s2IkxMVJqyJ0+mP960Kf1BU1OpLBujt+ido16+fDlKly4NS0tLeHl54dq1a5n2Xbt2LZo1a4aiRYuiaNGi8PHxSde/X79+kEgkalu7du0K+23oJ5nlU9esSWpIMTEUTaRhGns2xvrP1wMAFl1ahI7bOqLJhiaYf34+BIdg5Ahra6pxceIE5WXPn08ZU4mJVK0rbUpuQkI+C34o8qH79KHUrAyQSkkJ7cawjTiITqhr9RBxCVL8/DOtYQ8cqB4cZ7QMGEAV7NLq8Kdl1izab9xIf1hGPyn08X0BsmPHDmFubi42bNggHjx4IAYOHCgcHBxEaGhohv179uwpli9fLm7duiX8/f1Fv379hL29vXj9+rWyT9++fUW7du1EcHCwcouIiMiVXUYx9S2EENevqySqUlLUj3XqRMcWLdKObUKI+efmK9erFduwf4aJVFlByWgZF3I5/cknTKCVDwWTJwtRqhTtb93K5bJxWJhKiPzJk+z7t2xJa9kLFopDh4Ro2lQ1JS6VCvG//wmR5t+ZyQgfH/rAvvtO25YwaTDYNeqGDRuK4cOHK5/LZDLh4eEhfH19c/T61NRUYWtrKzZv3qxs69u3r+jcuXO+7DIaR52aqlpb/LgowOLF1P7ZZ1oxTQgh5HK5GPL3EIHZEKZzTYVktkS5dh2fnEGwDZMnqlVTX8+uVEmIGTOEuHs3h077/n0qrpEdr14JIZHQRV6+VDafP09fM8X1rayEmD5diOjovL8ngyA6Woj379O3X7hAH5SZGX2mjE5gkGvUycnJ8PPzg4+Pj7JNKpXCx8cHly9fztE54uPjkZKSAkdHR7X2M2fOwMXFBZUqVcLQoUPx/v37ArXdYDAxyVyJTLHWeP48IJNp0iolEokEyzosQ6eKnZAqT4W5ibly7drnDx+Ex4drxS5D4/p10tj48ksqkRkQQJVQa9ZMt+ScMdWqqdZWs2LHDvLFzZur5Qk3bQr88w9lajVurBLhKl8eWLkyc/EVg2bNGpKoW7gw/bEmTSiOJCWFCpszeofeOOrw8HDIZDK4urqqtbu6uiIkh5q2kydPhoeHh5qzb9euHbZs2YKTJ09i4cKFOHv2LNq3bw9ZFs4mKSkJ0dHRapvR0LIlaUZaWKi3165NCbnR0SSCoiVMpabY8dUOtC3XFkmyJMiFHDZmNrj06hKabWyGZFmy1mwzFKysSBBrzx7Kif7zT1pPtrAg+XcFcjmpol29So9zzbZttO/ZM8PD3t4Uv7h3L1ChAtkybBiVqN6/P5/r6PqGqyvFiKxfT4EFHzNlCu1XryYVHEa/0MAIv0B48+aNACAuXbqk1j5x4kTRsGHDbF/v6+srihYtKu7cuZNlv6dPnwoA4t9//820z6xZswSAdJvBT30LkXXZpA4daIrt5581Z08mJKUmiW67uynXqostLCZW31itbbMMmqgoIYKDVc8VM66AEB4eQgytdVEcb/mjSL52K/uTPXqkSi0KD8+2e3KyEL//TqWaFdds2pRqdRsFqalClCxJbzzN0p4SuVyIWrU0Uj+eyRkGOfXt5OQEExMThIaGqrWHhobCzc0ty9cuXrwYCxYswPHjx1GzZs0s+5YtWxZOTk4IDAzMtM/UqVMRFRWl3F69epXzN6LvpNN2TINi3lMDBTqyw9zEHFu7bsWw+sMAAO8T3uNR+COkyGheNCIhgiPCCxg7OyDtv6K1NdCtG6nLvn0LrLzTGG3OfA/nT6qhRw/g9u0sTrZ3L+19fEjfOxvMzEhdNDCQKolZWtJo28uL0ss+rrNtcJiYUF4bACxfnv64RKIaVS9ZQgXJGf2h8O8bCo6GDRuKESNGKJ/LZDJRvHjxLIPJFi5cKOzs7MTly5dzdI1Xr14JiUQiDhw4kGO7jCaYLC0pKUJ8HG1/7RrdsTs4FGDB4vwhl8vFrNOzlCPrZhuaifuh90W5JeXEt3u/FXHJcdo20eBJTBTi0Jzr4jusEc6SMOWIN+3kWGCgEI8fp3lRgwbUac2aPF3z1Ssh+vVTxaKZmwsxblyOBuf6S2govVGAwvU/JiWFan8DQixdqnn7GDUMNup7x44dwsLCQmzatEk8fPhQDBo0SDg4OIiQkBAhhBC9e/cWU9IUTF+wYIEwNzcXe/bsUUu/ivmvQH1MTIyYMGGCuHz5sggKChL//vuvqFu3rqhQoYJITEzMsV1G56j37hXC1pamutOSkkLtgBB+ftqxLRP+eviXsJ1vKzAbwmGBgzCZYyIwG6LGihricfjj7E/A5I9evYQAROqwkeLSJSFmzlS/lxs0iL42FSoIMWZAtDgOH5EICyH++9/OK7duqZQ0FZmFP/wgRGxs/t6OzvLf5yz698/4+MqVdLxkSfWcO0bjGKyjFkKIZcuWiZIlSwpzc3PRsGFDceXKFeWxFi1aiL59+yqflypVKsO15FmzZgkhhIiPjxdt2rQRzs7OwszMTJQqVUoMHDhQ6fhzitE5aoXepK1t+nxqHVqn/piA8ABRfUV1gdkQktkSYf2jtcBsCDtfO7HPf5+2zTNcYmOFsLGh70UmM1u9e1P2UNq0LxtpnOjUidaeP5aXzw1yuRBHjqiWaAEh3NzovLm4H9cPLl7878OzyfhuJCFBCFdX6rNli+btY5QYtKPWRYzOUctkQhQtSv/sH0frLFpE7Z06ace2bIhNihV99vVRToWbzzNXPh55eKRISEnQtomGx7Zt9J0oWzbLROuoKCH++kuIAe7/CHe8UcvTTsvly3nTIJfJhNi6VTX7qxhYrltnQINLuVyIX37JWtt7/nx689Wrc5ETLWKQwWSMDiGVZp9Pfe6c1vKps8LG3Aabu2zG4Z6HUdqhtFq61rJryzD91HQtWmegKOQte/akoKZMsLMDujYPx7rQz/EGxXHz7zfw9QVGjlT1SUqiUsvFipH0/Jw5wMWLOcudlkrJBH9/YMUKwMMDePmS6nJXrgwsW0bZhXqNRAKMHQuULp15n6FDqX7o/fvA4cMaM43JO+yombyRWYR3nTr0ixsVlU1Yr3ZpX6E97g+9jwneE2AioUh2CSRITElEZGKkdo0zNFq1ouTmTPKh1fj7b0Auh6R2bdTpWBxTplA0t4JXr8jBpqZSVPfs2SSA4ugIdOyoChbPCnNz8lWBgcAvvwDOzsCzZ8CoUaQZMnIk8PBhnt+tbpHRzbKDgypCPCOBFEbnYEfN5A1FgY7z59UrZmWlXqZj2JjbYFGbRbg1+BZ8yvpAQGD5jeUov7Q8Fl1chJmnZ+J19Gttm6n/jBsH3L0LVKmSfd99+2j/xRcZHi5fnopxPHtG2h3ffEOj69hY4NAh4PFjVd+wMBol37uXseCKlRUNPoOCaIRdpQqd5/ffSTytRg0aseul075/H/j8c8qPy4gxY+iO5fx5knhjdBqJEJxMml+io6Nhb2+PqKgo2NnZadsczSCXA05OwIcPJD3VsKHq2M8/AxMmAJ99RlqPeoAQAkcCj2DC8QnwD/dXtluYWGChz0KM8hoFSRbTtkwBEBNDw9ukJHLsNWrk6GVyOYnhnToFdOiguh/Yvl01iFdMlTdvTmqatWqllwQQgqpFLlsGHDmiPp3u6Qk0akRqaI0aAfXqkZ/TWR4+pLsNExOqmlWiRPo+331HSmaff06lRxmNkhu/wY66ADBKRw1QCT0TE6BvX6BUKVX7zZv0S2ZnB7x/D5iaas/GXJIqT8WWO1vww7kfEBSpUskoYVcCmztvRquyrbRonZ7x+jVw9izpixYpkn3/3btpiFy+PA2N83ljdPQoSVtfvJhe38PWlmbZFaXUP+bDB+DgQZJJPX4cSP5IedbKinTGW7akrVEjHfyat2hBsSKzZ6vKXaYlIIDuaoQAHjxQ139lCh121BrGaB11ZshkNISJiqIKDvXra9uiXCMXchx8dBBjjo3Bi6gXyvYaLjWwptMaNCrRSIvW6Qk//QRMngy0bUteMzt69qRh8MSJ9NoCIiUF8PMjn3X2LK1tR0fTfUTx4tRn0SJyyo0bUw2Lxo1pLRyg6fAbN4ArV4DLl2l79079GsWK0QRS5870dm1sCsz8vKOYUihenEbVGd1JdO1Kyw39+lHNakZj5MpvFGb4ubFgdOlZOUFRn/qnn7RtSb7Zfm+7KLawmFqd6/77+4vwOEOWuSoA6tal78CqVdn3TUkhNZKPJcsKgdRUKsmZlvbt1XO4Falb3boJ8euvQsSnqZIqlwvx4IEQK1YI8c03Qjg6qr/OykqIr74SYvduIeK0KXyXmCiEkxMZtX9/xn2uXOESmFqC07MYzfHuHfDXXxTdkxZFsJmOB5TlhO7VuyNsYhjmtJwDcxNamNx4eyOqLK+Cbfe2sWZ4RgQG0hKIiQmN2rLDz4+GuQ4O6vEOhYCJSfrl75UrKYts2DBav5ZKKXVr505g2jTSElewbRtw6xaliW3fDoSG0td87FigbFkqu7lnD/D114CLCw1WtZIAYWEB/O9/9HjVqoz7eHlxCUx9oPDvGwwfox5Rd+lCd+QLF6q337yZuXqZHhOZECkuvrwoqi2vphxdV1pWSTyLeKZt03SLH36gv/+nn+asv68v9e/SpXDtyiHR0UKcPElvY/Jk9WOVK6tGzw4OQvj4CDFlihB79pDOyM2b9JrSpdVH2i1a0MBWozL4gYF0cYlEiKdPM+5z+DD1KVJEiIgIDRpn3LAymYYxakf922/0T96+vXp7air9imWkXmYAJKUmicnHJyudtXSOVIw/Nl7I5PnQujQkatakv/26dTnr36YN9V+ypHDtyidyuRBjxgjh7S2EpWX66fJq1dT7Ll1Kq0BSqapPgwbpp94LlZEjSeP7vxoH6ZDLVX+vH3/UoGHGTW78BgeTFQBGHUx25w5QuzZF9X74oB6w0qULpX0sWEBBRQaGXMix4MICzD4zGylyyuVxtnbG+s/Xo2PFjsabzvXoEUUTm5rSvLCjY9b9k5OBokWB+PhcpWVpm5QUSle+fp1m7hVxk2vW0PHUVIouT0ykBAh7e/o4kpNp+n3KFGDGDJqh1jpbtwLffktz9c+fU1g7U6jkxm/wGjWTP2rUoB/Z2Fhak0yLQr3s7FmNm6UJpBIpvm/2PUImhKBxicYAgHfx7/D5js9Rc1VN3Au9p2ULtcS5c7Rv0yZ7Jw0A166Rk3Z2ptxfPcHMjIT4Bg0i8ZWbN2mv4N07yngyN6fl91evVGleMhnw449A3bqU/iWXUw53WJh23gu6daMUy7AwYPNmLRnBZAY7aiZ/SKWqZNTMdL8/Vi8zMBytHHFxwEVs7boVVqY0Erkfdh8LLizAh4QPWrZOCwwaRKOynKZYnT5N+5Yt6fukx6SdRHF3p5F2TAwFk23cSDKlzZsD1taUwvXwIaVzNW0K+PgArq60+fiQeNj69aQnFBOTT8NiYymgbMSIjI+bmgLjx9PjxYt1UqffmOGp7wLAqKe+AWDJEvpVad9eXeQ/K/UyA+V9/HuMPDIS2+9vBwC42LhgdovZCIwIhFcJL3xZ5UuYSE2yOYuR8ckndJO3YgWJcBsBQgDBwZS/vWJFekGVj5kyBfD1pccfPtCKUtWqtMJga5uDC754AZQpQxd+/BioUCF9n7g4GlW/f0/h7t98k+v3xeQcnvpmNEtmI+esqmwZKMWsi2Hbl9twtt9ZVHaqjLC4MAw7PAy/XPkF3fZ0Q4VlFfD7td8RmxyrbVMLh9zOnCQkkIIIQMU7jASJhARVfv2V/Gb//uqTCRUqkHiKjw+NzNOuCPj5UX8vL1r7LlEC+PRTGq2vXEmZcekoVYpupAHVIvrH2NioRtwLF5JTZ3QCdtRM/qlRg9a17t1Lr36UWZUtA6d5qea4Pfg2lrRbghK2Kp3loMggjDwyEs6LnNFvfz88i3iWxVn0DCEosPCzz6jSRU64fJm0vd3dgYoVC9U8XaVUKWDDBgpM69eP1r6fPKFR85s3wLx5wFdfqfqbmVEOt5sbPX/zBvj3X9IoHzZMFSIA0Lr5t98Cc+cCO6vOwS3URuzG3fSZZ8SIERRIdvMmiaczOgFPfRcARj/1nRW3b1PETUZR4UZCqjwVex7uge95X9wNu5vueGWnyqjrXhdlHMqglH0plLArodwcLB30J3r87l1SC7GwoEiqnMzJzpgB/PAD0KsX8OefhW+jHvDmDa0mrV6tqo/t5kYj5sGD1ePzPnygIHt/f1rvDgigil9169LxNWvoNR/j7hCPCjWtMW+eatIrLo5Wq2ynjgCWL9erojr6SKFqfZ8+fRqfKFSnPmL16tUYnNG3wsBhR50FRrhOnRlCCNwPu48jgUew/f523Am5A4Gs//0crRxR2akyKherjCrOVdCoRCPU96gPS1NLDVmdC2bOpOFfly6qcpXZ0bQpVc1Ytw4YMKBQzdM3oqOBtWuB334jXXIAsLSkpePBg6mSV3b3cHfvUthIQACN0h/fisO7eJUQ+enTqkmv9eupoJarUyrKhV9FWTxDueHtUbahE8qWpXuwHK2HMzmiUB21hYUFRo0ahfnz58PsP1298PBw9O/fHxcuXMCHD8YX5cqOGpQsunIlTWVu25ZxPvXChcCkSVozUdeIT4nH1ddXER4fjqDIIDyLeIY/7/2JuJQ4mEpNkSrPeL3X3MQcDYs3ROMSjVHLrRZqutZEpWKVYGZilmF/jVG1Kg3t/vyTRsjZERtLqX2pqcDTp6S/yaQjOZliu375RV2KtEYNoEcP+veqXDmHxcZev8aHkrXwRJRD4KL9aD/AA0WL0qE5c6jQVmacOqVSBj52jP6ly5ShrVQpoHRpuifXlwkgbVOojvrSpUvo06cPihQpgm3btiEoKAgDBgxApUqVsGXLFpRKW+7QSGBHDUrncHICIiPTj5x/+42EkD+OCmfUiEyMRI+/euD40+OQCzkAwExqhpquNVHaoTRS5Cm48voKwuLSJ9uaSc1Q2akyarrWRA2XGqjpWhM1XWvCw9ZDM1PnivrH5uaUi2tvn/1rjh0D2rWjX/mgIP6FzwYhKOV81Spy3AkJqmMVK5LD/vJLoEGDbD7KL76gm6P589OJy0RF0T1T4N/+eDZ7M56aVMRT728R9NocZ86oqtlOn0554B9jbU19duwAataktsBAinD39KRCXmZavp/UFQq9zGVsbCyGDBmCPXv2QC6XY968eZg0aZL+rKUVMOyo/6NzZyri+9NPVKpQAa9T54q3MW+x9e5WbLm7BffD7ivbv6vzHdZ0WoPAiECcf3ke195cw72we7gXeg8xyRkn2hazKkajbpeaqOZSjabRnSrDydopz/bJhRzRSdGISoxCVFIUIhIi4LR4Baov341HjSpgybTWeJ/wnrb494hNjkVcShziU+KRLEtGCbsSqOBYAeUfhaHaET90rv0N3NbtzLM9xsiHD1S+e/9+EkpJm95VsiTVQenWjSLD0/0sy2QkjZYVQtDN9o0bFIk2Y4ba4TNnKIAtKIi2Fy+At29Vx589o5E2oO7UpVJab/f0VG3jx6vKjcbHkyM3Bmde6I765s2b6NmzJ1JTU/H27Vt0794dy5Ytg41OFGHVPOyo/+PXX4Fx4zifugC5H3YfO+/vxPb727HisxVoU64NAODq66uYcXoGOlbsiA7lO8DMxAz3wu7hbuhd5fb4/WPIRMbCFQ6WDnC2doajlSMcrRxhbWaNVHmqcktMTVQ61/iUeCSmJqptH3NnBVAzDOjbBdhSO3fv0QRSfFq+DXrX7I0ulbvA2sw6l5+ScRMdTeW+9+6l2K+4ONWx6tVJf6Z3bypMlisU9axdXMgTW2YdF5GUROprz5+TBpLC2fr6UlT7y5cZ54underTplF/V1dy3mk3Dw+aDMj1+9BRCtVRL1iwALNmzcKgQYOwaNEiBAYGonfv3oiOjsaff/4Jb2/vfBmvj7Cj/o9btyjc1NYWiIjgdeoCRAgBAQGphDIqJ52YhEWXFimPVypWCR0qdMCnZT9F81LNYWNug4SUBDx89xB3Qu/gbuhdPAp/hEfhj/Ai6kWB2GRhYgF7S3s4mjtg8DUZWt+JxubpnVDE1RPFrIqhmHUxOFo5ws7CDjZmNrA2s4ap1BQvol7gybsABM4aibPFU3BVlb2GopZFMd57PEZ5jYKtBUcu5ZaEBJIk3b2bHLdietzKispu9utHTlQqBTnfY8fIk2dESgpQrhx53/XrVSUz84hcTqsir15RcNyrV7T98INK7/x//yMFt8wICqK1cIDW0zdupMy+tJubG+1btqRJPF2lUB21u7s7NmzYgPaK5HkAKSkp+P7777F06VIkZZafZ8Cwo/6PrEbOvE5doDyNeIr9j/bj0JNDOP/yvFrgmbmJOe4NvYeKxTLOS45LjsOLqBeISIhQbvEp8TCTmsFUagoTqQksTS2VztXKzArWZtawNLVUbnYWdvmLPFcU7rCywuPnfvjzwXb8cfcPPI98DoCi3Sd4T8CIhiPYYeeRyEiK61u9mnK0FZQqBQz+OgKTf3ODNDUFePCAAgEzYvFiWsaqVo10Egp5eVMup8y+N28y3g4cUA3s+/XLWpZcEfZw7x6wdCnN4ltaUhiFmRmNzD09aTTfqRPQuDEd0xSF6qjDw8Ph5JTx+tbZs2fRQqH7bESwo05DZiNnXqcuNKISo3Di2QkcCzyG48+OIz4lHqETQpWj7yH/DEFgRCCalmyKJp5N0KhEI+07P8W0aqNGSmUymVyGnQ92Yu7ZuQh4HwCApugH1xuMkQ1HorhdcW1arLcIAVy5AmzaREFeitzsfeiCLjiArS5jcP6LX1G2LDnxUqVoqrlYMcAqKRKSkp4UoX/8OEmg6QjBwTQpEBys2p49I8f88iU5/dxopNvZ0TgiKYmcuosLjTucnVWbkxOt/eckVjI7Cn2NmlGHHXUaliwhBz1mDDlrBWlH21euUJQLU+AIIRAWFwbXIq7K56V+K4VX0a+UfaQSKWq61oRXcS809myMPrX65P2CL1/SAmmXLvTLllMmTSKh66FDSew6DTK5DDvu78C8c/OUDttMaoYeNXpgaP2h8CruZbSBq/klIYEC0HbuBKzPHMa2qM8QgaIojjdIRPrSlhYWgKNpFIrEhcK8iDnMypdWjkhNTWlvbk6rXXZ2tFlZUVB5cjLNnicmkp+Pi6MtMZGcYVJS+jVrU1OSRC1ZkrYyZWiwX7lyxpU3w8OBS5coFf/0aSo1mhYzM3ptmTKkkKqwKz6eRu7h4RTpbm5OEuc54dUrsjG/sKPWMOyo0xAbSxGlGf1XKaLCFy9WVephChUhBPzD/XHm+RlcfHURF19eVFujruteF36D/JTPfc/7wq2IG2q71UYV5yrZT2//9BPVGvfxAU6cyLlhPj4UrrxmDTBwYIZd5EKOfx7/g58v/4xzL1S6mBWLVUSfmn3wbc1vUcrB+NJBCwqRKoOsdFmYvnmJ3Z224IBdb7x4oRql6lLBO6mU0uwdHcnRJieT03+RQbiFlxfw+ec0OlZkDGaHXE6pbwcOAHv2qPTSJRIKu6ldm/qEhwO7dmUbV5cj2FFrGHbUOWTRIhpJde5Mt/WMVngT/QaXX1/G9TfX4WHrgdGNRgMAklKTUMS3iHK920RigkpOlVDDpQaqu1RHE88m+KTMR6qEDRvSMGbVqoy1KjNCCJpdiYigOcZ69bJ9yfU317Hs2jL85f8X4lPile3lipZDY8/G8C7hDW9Pb1R3qQ5TKS+r5JgffqDUqyZNgAsXlM1C0D13RASNNONHTUHKxatI7vglkgeNQEoKOfKUFBoZx8bSlHpUFI3aFSlW5uY0Ki9ShDYbG3JyFha0mZmpFyNJTKRAs5cvaXvyhJbQIyIyfwtVqpD5TZtSyVCFBnp+OHeOUsqOH6fnUimpts2eTYFqBQE7ag3DjjoTUlPV16KvXCHdw2LFaN6Jpy91isjESPxw7gf4BfvhTsgdfEhUVxnsWaMntnbdCoD0y7tt6YTyW4+i0nug/Pp9KFeuAdxt3ZVr45ny8iUthJqa0i+8IuQ3B8Qmx2Kv/15subMFp4JOpZNgtTK1Ql33umhYvCFqu9VGpWKVUMmpEhwsHXJ8DaPi7VuaY5bJKOIsbZmutJw+TdXNrK1p7jet4HghIwRFiz98SKNoxQ2AuTlNaxcrVnjXvnaNVHEVkufW1sCECbTlV06VHbWGYUf9EefOUQUBDw/1CO/kZAq1TEig/7oqVbRmIpM1Qgi8jXmrzMl+GP4QLUu1RP86/QEAgRGBqLAsfU1jK1MrlClaBv1r98eExhMAkFO/FXwLJe1LwsXGBZIDByghtlYtdU3MXBKZGImrr6/i0qtLuPjqIq69uZap8IuLjQuJrDiWV24l7UuiuG1xeNh6aF9+VZt88QWpl6xfn3kNakVltLt3jTLF8sIFCn6/coWeu7hQudH8rFWzo9Yw7Kg/4s4d+qe2saHgsbQyQ598QrJGq1dnnr/J6Dzh8eHYPrARAiKf4nHjSnhqm4IXkS+UAivTm03HvFbzAADPPjxDuaXlAFDudfFUaxR/+QElipVB8U+/RJtybfBpOYomlslliE2OhZ2FXa4DxuRCjoDwAFx/ex3X3lzDw3cPEfA+AG9j3mb5OgkkcLFxgWsRV7gVcYOrjSvci7jD094Tnnae8LT3RGmH0nC00twoUqO8fEma69kNETdupETnEiUovNoY5MPSIATlpk+ZQmldJ0/mb1LQoB318uXLsWjRIoSEhKBWrVpYtmwZGmahdLV7927MmDEDz58/R4UKFbBw4UJ06NBBeVwIgVmzZmHt2rWIjIxEkyZNsHLlSlSokH60kBnsqD9CLqdchogISr1p1Eh1TFFhqXdvYMsW7dnI5I/QUFqsE4IiekqWRIosBS+iXiDoQxBK2pdEJadKAICbwTfx+fbP8TbmbYbVwtI69Sfvn6Di7xVhYWIB1yKucLFxgbO1M5xtnOFs7Yw25doo1dmSZcl48v6JUl3NwjTjKfSYpBg8fv8YgRGBtH0IxNOIp3gd/Rqvo18jRZ6So7fsYOmAskXLomzRsspqZlWdq6JSsUqwMssgeNLQSEykJYuwMMrz6tZN2xZphZQUCirL71p1bvyGXkVd7Ny5E+PGjcOqVavg5eWF3377DW3btkVAQABcMkgNuXTpEnr06AFfX1907NgR27ZtQ5cuXXDz5k1Ur14dAPDTTz9h6dKl2Lx5M8qUKYMZM2agbdu2ePjwISwLIrTPGJFKSf5o3z4aPad11E2b0v78ea2YxhQQ167R37lOHVrjBGBmYqacVk5LXfe6eD3uNVJkKXgT8wZvWjfA66RwvPl+OF4XM0fzUs2Vfd/FvwMAJMmS8DLqJV5GvVQ7l42ZjdJRP/vwDNVXVlceszK1QlGronCwdICDpQP61uqLQfUGwdbCFuUcy+HY02Ows7BDy1It0aliJ9ia28LG3AYpshTIhAzJsmSExoYiNC4Ub2Pe4mXUS7yKfoWXUS8RFheGyMRI3Ay+iZvBN9Vskkqkypri9dzr0eZRT/+kUIVQ1RTPCEtLSqebM4fSMI3UUZuZFVxAWU7RqxG1l5cXGjRogN9//x0AIJfL4enpiZEjR2LKlCnp+nfr1g1xcXH4J03x80aNGqF27dpYtWoVhBDw8PDA+PHjMWECradFRUXB1dUVmzZtQvfu3XNkF4+oM2DpUmD0aKBNG5IpVBATQ+vUcnnBJSQy2iE8nIKRFGWSckJYGAk5SyQUIpzBdGtCSgJC40IRGhuKsLgwvIt/h3dx7xAWF4Z25dspp8lvBt9Emz/a4EPiB2W1sbTMbD4Tcz6ZAwDwf+ePqisyUd8CMMZrDH5t9ysAioqvsrwKrM2sYWNO6myWJpaQSCQQQiinwR+GP8SDsAfpgu4Act6l7Euhvnt9dK3aFc1KNoNbETdcf3sd5ibmGW5WplawMddSvYTUVAr0vHGDpIBr1864X0gI3ZilpAA3b9KNGpMnDHJEnZycDD8/P0ydOlXZJpVK4ePjg8v/KRt9zOXLlzFu3Di1trZt22L/f6lBQUFBCAkJgY+Pj/K4vb09vLy8cPny5Rw7aiYDFIVrL1ygf2rFepatLf0I3LxJx/gz1l+cnGjLDbdu0b58+UzXRK3MrFDaoTRKO5TO8lR13esifFK4sppXREIEohKjEJkYiQ+JH9QkVG3MbTCgzgBEJ0Urt9jkWMQmxyImOQZFrYoq+yraYpJjgLj01/X29MbS9ksBkFMv8Wv6m025kCMoMghBkUHY7b8bAFDaobRSIjUjulXrhh1f7QBAa/VWP1rBVGoKMxMzmEnNYGZCEq+mUlO0LdcWqzquUr62yYYmkAs5TCQmShlYU6kpTCQmqOteFz+0+kHZd+DBgUiUJUIqkcJEYgITiQk9bvwB5cyA8atXU215AD+c+wExSTEwkVIfqUQK6eCKkN57APdNIzGwjiqla/3N9fiQ+AFSiRQSSGgvoX1Ry6LoVVNVo3yv/15EJERAAgkkEona3sbcBl9V/UrZ98TTEwiPD1eeD4Cyv7mJOT6v9Lmy76VXl/Au7p1aPwCQSCQwkZigfQWV9PXN4JsIiwtT65P2Na3LtlZmMNwPu4/Q2FAAgKWpJZqUbJLp37Ew0BtHHR4eDplMBldXV7V2V1dXPHr0KMPXhISEZNg/JCREeVzRllmfjEhKSlLTNI9WaPIxKqpVo7yJ9+/pLj1tsZZmzdhR6zM5KZOYGQpHXbdugZkjlUiV092ZUdK+JNZ9vi5H5ytTtAyejHyCuGSqHqaoIpaQkoD4lHhUdqqs7GtlZoWxjcYiKTWJKovJEpGYkojIpEiExYbBwdIB8anxuB1yO52TNpWaQgIJ5EIOmZCpRZ6nyFOUW0JqAj7m45rkV15fyXBWAYCaDjwA7Hq4C9FJGfxmOQJNqgDj//yThGxsbbH8+nKExH70W+gE4BOgduglDPzwgQLRACy4uACBEYEZ2lDBsYKao55zdg7uht7NsK+HrYeao555ZiauvL6SYV8HSwd8mKya0Zh5eiZOBp3MsK+Z1AzJM5KV6n1T/p2CE88yF+lJmZGidNTzz8/H9vvbAQAl7Erg1dhXmb6uMNAbR61L+Pr6Ys6cOdo2Q7eRSmkNKzqakg/T0qwZrXHxOrV+0q0bBZMtXEiVDHKDwlHr8JSpuYl5unX2zHC0csQvbX/Jtl9UYhQuvbqE089P40DAATx+/1jpQCWQoFnJZqjrVhcvo16ipH1JWJhY4NXYV0iVpyJF9p/TlqUoy5DaW6qLTR/sfhCp8lTIhIz2cpnyuXsR9QVV39a+SEhJUN4gyIUcMrkMMnkqPH9aBcSGkRb7oEEYWn8oIhMjIRdyCCH+e00qxK5dKP48giLB/5u17FKpC0LjQqkvhLK/XMjT2dCyVEuUsi+l7CcglNcoZq2eGF3fvT6sTK3U+ir2RczVy2NVda6KuBSaBkl73ujEaMSlxKH91vbKkXRapJDCwtRCWYDGytQKaVeFPe08Ud2F4iFcbdQHdppAb9aok5OTYW1tjT179qBLly7K9r59+yIyMhIHDhxI95qSJUti3LhxGDNmjLJt1qxZ2L9/P+7cuYNnz56hXLlyuHXrFmqnWZNp0aIFateujSVLlmRoS0Yjak9PT16jzimhoSQfJJFQZLihFJg1BuLiKKI/ISHrtczMqFCB9BmPHaP4BSPlUfgj7H+0H3v99+L6W3WBak87T3iV8EKj4o2Uwi0aK6Ly88+k5lGnDiUKZ5Z/tGYNKdGVKwc8fqwuL6YDCCFw/e11/Hn3T+y4v0MZpKhAMT2fWb12gGYB+tTqg941exeKVK3Bpmd5eXmhYcOGWLZsGQAKJitZsiRGjBiRaTBZfHw8/v77b2Vb48aNUbNmTbVgsgkTJmD8f9rT0dHRcHFx4WCywqZiRdIHPHQISJMux+g4e/cCX35JVQ6ePs1dIml0tKrsUFgYOXwGL6NeYq//Xux5uAeXX19ON4UtgQQVilVAXfe6qOhYEZ72nihhVwKedrTPS855poSHU4BnUhJF9jdokHG/uDgqsRUVBRw5ArRrVzDXzyep8lTsfrAbP136CbdDbivbna2d8Xmlz1Hfoz7quNVBDdcasDK1QrIsGbHJsXif8B53Q+/iZvBN+AX74eLLi8qROQC0LtMaYxuNRfsK7bNX3sshufIbQo/YsWOHsLCwEJs2bRIPHz4UgwYNEg4ODiIkJEQIIUTv3r3FlClTlP0vXrwoTE1NxeLFi4W/v7+YNWuWMDMzE/fu3VP2WbBggXBwcBAHDhwQd+/eFZ07dxZlypQRCQkJObYrKipKABBRUVEF92YNBZlMiNu3hXj5Ur29f38hACHS/L0YPaBXL/q7jRuX+9eePUuvLVGi4O0yEGKSYsTpoNNiwfkFovP2zqLELyUEZiPLrcj8IqLy75VFuz/biR/P/SguvbwkklOT827Et9/m7G88ejT169gx79cqIKITo8XvV38XZX4ro/xcrH6wEj329BCHHx/O9ecRkxQjNt/eLFpvbi0ksyXKc1b5vYpY57dOJKTk3D9kRm78hl45aiGEWLZsmShZsqQwNzcXDRs2FFeuXFEea9Gihejbt69a/127domKFSsKc3NzUa1aNXHo0CG143K5XMyYMUO4uroKCwsL0bp1axEQEJArm9hRZ4HCIc+Zo96+YQO1N22qHbuY3JOUJIS9Pf3dLlzI/et/+41e26lTgZtmyITGhoqjT44K3/O+YtDBQaLD1g6i5sqaouiColk678+3fy523d+Ve6dy754Qhw4JkZqadb+AAPp7SiRCPHuWq0vI5DIRGhsq7oTcEQ/DHoqXkS/F+/j3uXKocrlcnH1+VvTb30/Y/GijfO9OPzmJuWfmivfx73NlU2Y8//BcTDw+Udj52imv4brIVbyNfpuv8+bGb+jV1LeuwlPfWbB6NTBkCNCyJQn7K3jyhKa/zc1p+ozFZXSf48epPJGrK+VP53Zd8n//o+CjGTOAuXMLx0YjIz4lXqmw9vDdQ5x+fhpnnp9BRIKq3JS9hT2+qfYNelTvgaYlmxasrnmbNlTedPJkYMGCDLt8SPiAK6+v4NKrS7jy5gqeRjzFm5g3SJYlZ9jfydoJHrYe8LD1gFsRN1Kms3aGk7UTYpJjEBgRiKcfnuJe6D21OusVHCtgtNdo9K/Tv1DEZqISo7Du5jr8dvU3eNp54tKAS/k6n8GuUesq7Kiz4PFjoFIlcsiRkao61UKQvE9oKKVpNdFsXiKTB4YOVZWzXLUq+/4fU78+BSjt2UPr3EyhIBdy3Am5gz0P9+CPu3+oOTN7C3u0K98OHSt2RDXnanC3dYeztTNMpJmk2yUnU5WzTG7KxL59SPqmK6KKOyHy8ilEyuLxJuYN7oXew72we7gbehdPIp5k+FoJJHCydoJMkL57Zo47K4qYF0G3at3Qv3Z/NPZsXHBr9VmQIktBSGwIPO0983UedtQahh11FghBwSlv35KKfatWqmNdu5LMqBFW49FL/vqL9NlHjQJat87da1NTqSBxUhLNppTPWfoTkz/kQo4zz8/gj7t/4J/H/yA8PjxdH6lECidrJ1iZWsHC1EKplPa/o6Ho8W8oZg+sgMuVbRCXHIe4lDjEJcchMTURybLkHOukV3CsgMaejdHYszGqOleFp51nuqplybJkRCVGITg2GG9j3uJtzFuExIYgPD4c4fHheBf/DtZm1ihflGRqyzmWg1dxL+2pueUTg1QmY/QUiYRUyrZuVdW0VdCkCTnqixe1Zx+Tc778Mu8j4SdPyElbWwNlyxasXUymSCVStCrTCq3KtIJMLsPVN1fxz+N/cOLZCbyKeoWwuDDIhTxdXjEA9H8NOMYALY74Y1k24w+JAOxTTeHgXALO1s6o5lINNVxqoIZLDdR2qw1nm+wj/M1NzKn4io0zarrmQpbWCGBHzRQ+rVqRoz51iipnKVBMd1+6RCNvDUxbMVri7n8qVDVq6FzOrbFgIjVRjmrnt54PgNKZwuLC8C7unXKUnCRLQrIsGTb1nwMdh+OLx1KcaLUBpp6lYGNmAxtzG1iaWsLcxBwWJhYwDw6FbaWakMpTgYBjFHvCFCjsqJnCR6H7fe0aEBtLU6AAyUhaWlLupmItm9E9hKCgwDZt8j4avnOH9plVZmK0gqnUVBm4lY4KAJrvhPTcOficDAJm9834JOWdgfYdSBNhzRpg8eJCtdkY4VtbpvApUwaYP5+EESzS1Aw2N1cJKvD0t+5y7x4FklWrBsTH5+0cihF1biptMdpn6FDar1lDxXUyY/Bg2m/aRHWrmQKFHTWjGaZOBXx8VFW0FCimv9lR6y779tG+TZv0uu05ReGoeUStX3TtSul4wcGq70FGtG9PQaPv35N6HVOgsKNmtIuiqAM7at1F8QP9xRd5e31EBNUeB2iNmtEfzM1Vo+X/pJszxNQU+O47erx6deHbZWSwo2Y0gxDAP/8A48eTwIkChaMOCKC1aka3ePaM1pdNTIBOnfJ2jnv3aF+qlErrm9EfBg9W5dBnxYABFCh47hzg768Z24wEdtSMZpBIqBzeL7/QP7KCYsWAyv/V972UP6UfphBQjKabN6e/VV7gaW/9xsMDWLGCYhSyokQJoGNHerxmTeHbZUSwo2Y0hyKH+uRHhd15nVp3ye+0N8CBZMaEYpp882YqhcoUCOyoGc2hULNiR60fREcDt2/T4zQ14HONIjWLHbV+c/s28O23wJIlmfdp2xYoWRL48IGkYpkCgR01ozkU+dT375PGtwKFo75xg9SrGN3Azo7+TsePA5551DWWyejvDfDUt75z8yYJF/32G/1dM8LEBBg4kB5zUFmBwY6a0RxOTkDt2vT41ClVe4UKgLMzOembN7ViGpMJNjbAp5/m/fVPn9IUqJUVUK5cwdnFaJ4ePQBHR+D5cxI3yYz//Y8c9sWLwIMHGjPPkGFHzWgWxTp1WkctkXCalq5RULV6FNPeNWrQjzejv1hZqVKwli7NvJ+HB/D55/SYR9UFAjtqRrMo1qkfPlRv53Vq3WLHDpqqzu8PLQeSGRbDhtEN18mTqrS7jFAElW3Zknc1O0YJO2pGs3zyCeVYXrig3p7WUXPlVe2zbx852Rcv8ncedtSGRalSpFYG0Fp1Znz6KUkHR0UBu3ZpxDRDhh01o1msrChv+uNKWfXqkQ74u3dUEpHRHomJpMsO5C8tC+BiHIbImDG037oVCEtfHhMACZ9wUFmBwY6a0Q0sLICGDenxx6NtRrP8+y9VOStenG6g8kpUlGpEztKhhoO3N9UlnzuXqt9lxv/+R9KiV64At25pzj4DhB01o3lCQ4Gvvyalo7TT3E2b0p4dtXZRiJx06ZK/2tGKHGxPT6Bo0fxaxegKEgnlSE+aRCl8meHqCnz1FT3OSiecyRZ21IzmKVoUOHyYAsoUObYAO2pdIDUVOHiQHud32vv4cdorIvoZ42PkSNpv28Za/vmAHTWjeczNgWbN6HFalTJvb7pbf/JEXRCF0RwXL9IPatGipO+dHxS5tp99ln+7GN0jJYWyA/r2zTwA1NsbqFuXNBLWrdOsfQYEO2pGO2QkJ1q0qEr4n9O0tIO9PclE9u6dvnZ4bnjzhgLJJBKgXbuCs4/RHWJjqWLWli3A6dMZ95FIVKPqFStoxobJNeyoGe2gcNRnz9KduQKe/tYutWsDf/yRtZ5zTjh8mPZeXqQ6xxgeRYsC/frR48WLM+/XvTupEr56pVpWYXIFO2pGO9SuTWUTY2KAa9dU7eyoDQOe9jYOxo2jgMMjR1Q58x9jaalK1eKgsjzBjprRDlIp4ONDjxVBR4DKUd+6BcTFad4uY+bkSYrUzq/gTFISpXgBQIcO+TaL0WHKlVNFdi9alHm/oUNJ0ezMmawVzZgMYUfNaI927Sh3Om1lppIlqQB9aqr6SJspfEaMAOrUAfbuzd95zp6lmyx3dzofY9hMnkz77dszV7Lz9FRlEfCoOtewo2a0R79+wNWrKqF/gIJPePpb8/j7A48eUUR+fqplAapp7w4d0ivQMYZH3bo0OyaTAb/8knk/RVDZn39yqlYuYUfN6B7sqDWPQuSkdeusRSyyQwhenzZGJk+mVKy2bTPv06wZOfWEBJYVzSXsqBntEx2tHoiicNSXLnE6h6ZQTHcrCi7klSdPqAa1mZkqBoExfFq3ppTKrGISJBIKPgOA33+nWAYmR7CjZrTL+fNUjD6tg6henUZ1sbEceKIJXrwA/PwowE9RRzivKEbTLVoAtrb5t43RDySSnC1zfP01aciHhNCaNpMj9MZRR0REoFevXrCzs4ODgwMGDBiA2NjYLPuPHDkSlSpVgpWVFUqWLIlRo0YhKipKrZ9EIkm37dixo7DfDqOgdm36B3/6FHj2jNpMTFSykzz9Xfjs30/7pk0BF5f8nSvt+jRjfERGAvPmAevXZ3zc3Fy1Vv3LL1zSNofojaPu1asXHjx4gBMnTuCff/7BuXPnMGjQoEz7v337Fm/fvsXixYtx//59bNq0CUePHsWAAQPS9d24cSOCg4OVW5cuXQrxnTBq2NrS2hYAnDihaldMf587p3mbjA1Felx+p73fvVP9vXh92jjZsweYOROYMYPKpWbEoEGAjQ3NlqVVJmQyR+gBDx8+FADE9evXlW1HjhwREolEvHnzJsfn2bVrlzA3NxcpKSnKNgBi3759+bIvKipKABBRUVH5Oo/RMneuEIAQXbuq2s6dozZXVyHkcu3ZZgwkJwtx/LgQwcH5O4/i79igQcHYxegfSUlClCxJ34NlyzLvN3Ik9WnfXnO26Ri58Rt6MaK+fPkyHBwcUL9+fWWbj48PpFIprl69muPzREVFwc7ODqampmrtw4cPh5OTExo2bIgNGzZA8HSMZmnThvanTqmCxxo0oBrVoaEUoMQUHmZmlJLl5pb3cyQmUoAQoAoYYowPc3Ng6lR6vGBB5gFjo0fTkteRI1RFj8kSvXDUISEhcPlo7czU1BSOjo4ICQnJ0TnCw8Mxb968dNPlc+fOxa5du3DixAl8+eWXGDZsGJZlk5CflJSE6OhotY3JB/XrAw4OtL514wa1WVqSTjTA09/6wPbtQFgYCVt8+aW2rWG0Sf/+JFr05g2wYUPGfcqVo3rnQNa51wwALTvqKVOmZBjMlXZ79OhRvq8THR2Nzz77DFWrVsXs2bPVjs2YMQNNmjRBnTp1MHnyZEyaNAmLspLCA+Dr6wt7e3vl5plWWYvJPSYmqiIdadepFWUW2VEXDu/fAxUqAOPH5y8NTgjVj+2oUfmrusXoPxYWwJQp9NjXN/NR9fjxtP/jDy5rmw1addTjx4+Hv79/llvZsmXh5uaGsLAwtdempqYiIiICbtlM18XExKBdu3awtbXFvn37YJbNj4iXlxdev36NpCxy/KZOnYqoqCjl9urVq5y/aSZjBg2iMnh9+qja2FEXLgcOAIGBpMv90XJQrvj3X+D+faBIEXWVOcZ4GTCAJGRfvQI2b864T+PGQKNGQHIysHy5Zu3TM/Lx35l/nJ2d4ZyDEnje3t6IjIyEn58f6tWrBwA4deoU5HI5vBTToxkQHR2Ntm3bwsLCAgcPHoSlpWW217p9+zaKFi0KCwuLTPtYWFhkeZzJA4p16rQ0bkwO5MUL2kqV0rxdhsxff9FeUVQhryhG0//7Hy1hMIylJTBtGun1f/JJxn0kEmDCBPr+LV9Oo3Bra83aqSfoxRp1lSpV0K5dOwwcOBDXrl3DxYsXMWLECHTv3h0eHh4AgDdv3qBy5cq49l8hh+joaLRp0wZxcXFYv349oqOjERISgpCQEMhkMgDA33//jXXr1uH+/fsIDAzEypUrMX/+fIxU5Pkx2sXGBvjvxoxH1QVMVJRqmSE/a8oPHgBHj9KP7ujRBWMbYxgMH06j6QoVMu/TpQtQtiwQEQFs2qQpy/QOvXDUALB161ZUrlwZrVu3RocOHdC0aVOsWbNGeTwlJQUBAQGIj48HANy8eRNXr17FvXv3UL58ebi7uys3xVS1mZkZli9fDm9vb9SuXRurV6/GL7/8glmzZmnlPRo9oaHAypWq6GGAp78Li3/+AVJSgMqVgapV834exWj6iy/oB5dhMiOjbBoTE2DsWHr8yy9U2INJh0RwLlK+iY6Ohr29vTL9i8kjx4+TqL+HB/D6NY3S/vkH6NQJqFgRCAjQtoWGQ9euVIhj+nRSksoLFy7QjZQQpPOsUJNjmLQ8fUrT4DVq0P5j4uIoW+DDB1qOya/wjp6QG7+hNyNqxgho3pzWqN6+VRXpaNqUHPbjx6QPzOSf2FjKXwXyPu0dH09pOELQ2jQ7aSYzbtwAdu4EFi4k9bqPsbEBhg2jxz//rFnb9AR21IzuYGmpCjxROBIHB6BWLXp8/rxWzDI44uPJuTZpovpsc8u0aRQxXrw4/7gyWfP111TeMiaG0rUyYsQIEku5dIlmZxg12FEzukX79rRXOGpAtU599qzm7TFEXFwoyvbChZxVPPqY8+eBJUvo8dq1HOnNZI1UqnLQy5dTBsfHuLmpUjNnztScbXoCO2pGt1A46osXKTIZ4IAyXUIxGldMeSv+XgyTFZ9+CrRqRTnTmTni6dNpVH3qFOXmM0rYUTO6RdmyFDgmk6n+WZs1o/29e5TGweSdW7fohicv0bVyOTB4ME15lyjB0o9MzpFISPsbALZsofzqjylVChgyhB5//z2XwEwDO2pG92jfntI2AgPpuYsLpREBvE6dXxYuBFq0AObOzd3rhKA0mj//pL/Nxo2AvX3h2MgYJg0aqKa3FU77Y6ZNo+Cy69cpK4EBwI6a0UWmTiUd6smTVW0tW9L+9GmtmGQQxMcDf/9Njzt1yt1r584Fli6lx5s2AT4+BWoaYyQsWADMmkX63hnh4qLKq54+nfOq/4MdNaN7uLqmH621akV7LjSfdw4fJmddpoxK8S0nLFkCKIrZLFsGfPttoZjHGAHu7vRdsrHJvM+ECYCjI+Dvn7lDNzLYUTO6TUoK7RVpW/fvc6WdvLJrF+2/+Sbn0d4rVgBjxtDjuXMpjYZhCgK5POO1ant7VfWtWbOo1rmRw46a0U38/KiyjqJYh5MTULs2PT51Smtm6S1xcaTyBpCjzg4hgPnzSa8ZoJKE06cXnn2McREdDTRsSLn8GZUyHj6cFApfviRnbeSwo2Z0Eycn4OpVilB+/57aFNPf7Khzz+HDQEICUK4cUKdO1n2FACZNUsk9zpwJLFqUt5xrhskIOzuaBk9NJVWyjyO8ra1pNgcAFi8mIRQjhh01o5uUKgXUrEnTYwrxk9atac/r1LlHUSnr66+zdrgyGaVgLV5Mz3/5BZgzh500U/AsWQJYWVGA6Nq16Y937gz07k2/Af36UXyFkcKOmtFdFJHJBw/Svnlzqk8dFEQbk3NWrQIuXyYnnBkyGYmYrF1LalLr16sicBmmoClblpZXAAog+6+qoRpLlpBM7ZMnlA1ipLCjZnSXzz+n/dGjpGhUpAjg5UVtPP2dO6RSWvMvXTrj4zIZjVq2bKE86e3byWkzTGEyciTg7U064IMHp58CL1oUWLeOHi9darTpmeyoGd2lfn3SAI6JUcmHcppW7pHLsz6emkpCFAoxkx07chZwxjD5xcQE2LABsLCgJa6M0rHatQMGDqTHffoAb95o1kYdgB01o7tIpcBnn9FjxfS3Yp361CmWGMwJkZEk9zl4MJCUlP64EMB33wHbttGywq5dwFdfadxMxoipXJlyq+vXzzzQ8eefSVr49Wty3B8+aNREbcOOmtFtvvqKaiYrRtKNGlEASmgo8OCBdm3TB/buBYKDKWrWwiL98S1bgM2bVU66a1fN28gwEyZQDEWNGhkft7UFjh2jSPH792lZLCFBszZqEXbUjG7Trh2wZw/QpQs9t7AAmjalx7xOnT3bttG+Z8/0x4KCaI0QoMjuL77QnF0MkxZTU9oUZFQKs3Rpilext6cSrd2707KNEcCOmtE/OE0rZwQHq4JvundXPyaTUepLTAyJTqTVVWcYbSEETYOXL5/xjXjNmrQMZmFB+969jWJkzY6a0Q8CAlSBJgpHfeaM0dxR54lduyiQrFEj0vdOy8KFVPPb1pY+VxMT7djIMGmRSGgdOjWVZoFCQtL3ad6cshIUgY8tWgBv32reVg3CjprRfUJCKOCkb19am65TB3BwIBnCq1e1bZ3usn077T+e9r5xQyXL+Pvv6Z04w2iTpUuB6tXpf71Xr4xvxr/4Ajh+nIp3XL9OgWgZ6YYbCOyoGd3HzY2qPQlBZRpNTGjtGgAOHdKubbrKs2d0EyOVqqdaCUHr0qmppFLWu7f2bGSYjLC2Bnbvpgpbp06R7ndGGR6tWpFzrlqVlnmaNwd++80gZ9nYUTP6gSLQae9e2nfsSHtFfWVGHUtLUnLq14/Khio4dw64coXW+JYuZWlQRjepXJny+qVSYM0a4McfM+5XrhxFi3fqROmHY8eSKNKNG5q1t5CRCMHJqPklOjoa9vb2iIqKgp2dnbbNMUwePQKqVAHMzICwMFp7dXamfVBQ5opbjDrt2lGay9ChqqIHDKOrrFihquB282bmedZyOSmYTZ5M2gFSKRX7mDmTfid0kNz4DR5RM/pB5cpAtWpUn/rvv2ltqkkTOqYo38hkza1b5KSlUspbZRhdZ9gwquK2Zk3WVd+kUmDQILqh79mTHLci/mLqVFUFPj2FHTWjP3z5Je3/+ov2iqId7KjV+fNPuplJSVFvX7iQ9t27U0EEhtEHfvhBJSEKZL0G7eoKbN1K1eLq1aM67AsW0Izb99/TWrYewo6a0R8UjvrCBSrSoVinPn2a8oEZ+hGbOJGUm44eVbUHBlKADsA504z+EhFBDnjTpqz7+fhQNPiBA0Dt2kBsLODrSw67f3/g7t28Xf/QIWDevLy9Nh+wo2b0hxo1gMOHqRyeuTlNh5crR07733+1bZ1ucOIEpbM5OQFt26raFy2i6cDPPiPRCIbRR9asISfbvz99p7NCIqEb1ps3gf37SdEwOZmcfK1aFCW+bh0QFZX9dYWgkXmnTrTunfYmWAOwo2b0B4kEaN+etL4Vzzn6W50tW2jfsyfdzAA03acYgUyZohWzGKZAmDyZZowAYNIkCoqMj8/6NRIJ0LkzcP48pSx260YpnufP05S6qyu1HTlCin0fIwTw7be01i0EMGSIqvaAhmBHzegvQqjWqQ8dyr6co6ETFUUjB4DKASpYvJhGEk2bqnTSGUYfkUiAn35SjaZXrQLq1s15OlbDhqRm9uIFxWxUq0ZpXbt2AR06ACVLkkN+/Fj9mrVqkRb5ypW0KW6CNYVg8k1UVJQAIKKiorRtinGwdKkQVaoIsXevEElJQtjaCgEIcfWqti3TLmvX0udQtaoQcjm1BQcLYWVF7UeOaNc+hilIjh0TwsODvtumpkLs25f7c8jlQty8KcTo0UIUK0bnUmwNG9I5U1Ko34MHBWp+bvwGj6gZ/eP5c8Dfn6K/zc1Va7HGPv2tmPbu21clZPLTT1S0oFEj9TVrhtF32rQB7t0jhT13d6BlS9WxnMqDSCSU9vXbb6QXvn49xb4ApHr2xRcUB7NwobpwkIbRG0cdERGBXr16wc7ODg4ODhgwYABiY2OzfE3Lli0hkUjUtiFDhqj1efnyJT777DNYW1vDxcUFEydORKoBStAZFIro74MHadpKMf1tzI46Pp4i36VS0kcGKKhs5Up6PGcOq5AxhoejI7BzJ0V4OzhQm1wOfPIJ6dlfvZo+TfFjkpOpwE+/frT+/OgRtTs70zlfvqTpcE9P9eOapEDH8oVIu3btRK1atcSVK1fE+fPnRfny5UWPHj2yfE2LFi3EwIEDRXBwsHJLO82QmpoqqlevLnx8fMStW7fE4cOHhZOTk5g6dWqubOOpbw0jkwlRvDhNT+3bJ8S7d0KYmNDzgABtW6ddnj5VPR47lj4Tb2/VVDjDGDqHDqlPYdvYCNG2rRDjxwvx/fdCXL6s6vvnn0KYmaWf8t6wQYjkZCESEoTYuFGI2rXV+7RvL0RYWL7MzI3f0AtH/fDhQwFAXL9+Xdl25MgRIZFIxJs3bzJ9XYsWLcTo0aMzPX748GEhlUpFSEiIsm3lypXCzs5OJCUl5dg+dtRaYPx4+of56it63q4dPZ8zR7t26Qpv3wphaUmfybFj2raGYTRHSooQ27cL0aWLEEWLqjtYQIjly1V9z5+ntqJFhRg4UIgbNzI+p1wuxJkzQnTuLIREIkTZskKkpubLTINbo758+TIcHBxQv359ZZuPjw+kUimuZlPmcOvWrXByckL16tUxdepUxKcJ5b98+TJq1KgB1zRrD23btkV0dDQePHiQ6TmTkpIQHR2ttjEaRjG9+/ffFO3cowc937495+tThsLr11TyMy0//QQkJgLe3sCnn2rHLobRBqampL63bx8QHg7cvg0sWUKyuSNHqusINGhAtQLev6cc7Xr1Mj6nREJ1r/fvB548ATZs0GgNd1ONXSkfhISEwMXFRa3N1NQUjo6OCMmosPh/9OzZE6VKlYKHhwfu3r2LyZMnIyAgAHv/q8AUEhKi5qQBKJ9ndV5fX1/MmTMnr2+HKQhq16YiHf7+VFHryy+pItSjR8CdO3TcWJg4kdbrV62ispVv39JjgNemGeNGKqXUqlq1Mj5uYZH7gj7lytGmQbQ6op4yZUq6YK+Pt0f5WLgfNGgQ2rZtixo1aqBXr17YsmUL9u3bh6dPn+bL7qlTpyIqKkq5vXr1Kl/nY/KARAIMHkwRztWrA3Z2pLoF0KjaWAgPpxuV+HjKCQVI4jAxEWjcmKQUGYbRa7Q6oh4/fjz69euXZZ+yZcvCzc0NYWFhau2pqamIiIiAm5tbjq/n5eUFAAgMDES5cuXg5uaGa9euqfUJDQ0FgCzPa2FhAQsLixxflykkRo9Wf96jBzmtHTtI7s8YRpJbtlDUat26tD15AqxdS8d8fY3jM2AYA0erjtrZ2RnOOagV6u3tjcjISPj5+aHef2sIp06dglwuVzrfnHD79m0AgLu7u/K8P/74I8LCwpRT6ydOnICdnR2qVq2ay3fDaJ3PPgNsbSmd4vJlGlEaMkKonLKiutDMmSSD2KEDaRkzDKP36EUwWZUqVdCuXTsMHDgQ165dw8WLFzFixAh0794dHh4eAIA3b96gcuXKyhHy06dPMW/ePPj5+eH58+c4ePAg+vTpg+bNm6Pmf8EEbdq0QdWqVdG7d2/cuXMHx44dw/Tp0zF8+HAeMesLQpDo/uLFpAHepQu1G8P098WLtCZvbU3a3rdu0WwCAMyfr13bGIYpOPIVX65B3r9/L3r06CGKFCki7OzsRP/+/UVMTIzyeFBQkAAgTp8+LYQQ4uXLl6J58+bC0dFRWFhYiPLly4uJEyemC4V//vy5aN++vbCyshJOTk5i/PjxIiUlJVe2cXqWFgkPJ/lAQAh/f1UOpYsLpWkYMr1703v93//ouSJFrWdP7drFMEy25MZvSIQwtlyWgic6Ohr29vaIioqCnZ2dts0xPjp2pKIc06fT1K+7O6VbnDhhuMFU8fGAiwsQF0fT/ImJpMZkakqjbA1HpTIMkzty4zf0YuqbYbJEkVP955+U2/jVV/R861bt2VTYWFuTQ166lCoCTZ1K7YMHs5NmGAODR9QFAI+otUx8PODhQcInJ07QWnXTprR/+1alAWyo7NlDhQmsrYGnT4FcZEIwDKMdeETNGBfW1qpR9dq1FO1dvTpVjfrjD+3aVhikLTKQnAxMmUKPJ05kJ80wBgg7asYwGDSI9grZQEWVtFWrDE9S9JtvgHbtgLt36f0pRtETJmjbMoZhCgF21IxhUKsW6fYWK0Zrt99+SyPthw+B8+e1bV3B8ewZcOAAcOwYBZAppGznzAGKFNGubQzDFArsqBnDYc8eEjtp1gywt1dNhyt0rw2B5ctphqBtW+Cvv4CICNI8/9//tG0ZwzCFBDtqxnAoWRIwM1M9HzyY9nv2AB9J0OolsbHA+vX0uHt3qggEUKUsU72or8MwTB5gR80YHjIZlbarV4+mw1NSgI0btW1V/tmyhSLbK1YETp4EkpKAli1VxUgYhjFI2FEzhkVoKFCmDNCoEU0LDx1K7atXA3K5dm3LD3I55UwDlCeuyBFftIgLbzCMgcOOmjEsXFwAR0cabf7xB9CtG61XBwVRAJa+8u+/QEAAlfN88IDWqbt2BerX17ZlDMMUMuyoGcNCIlGlai1fDlhaAv370/Mff9TfVK2WLYF164ABAyjqWyJRRXwzDGPQsKNmDI8+fUiN7MkT4O+/SQjE0pKqTR0/rm3r8oa5OTnpwEB63r07ibowDGPwsKNmDI8iRVSCJ4sXk7yoYq16xgz9G1Ur1tavXqUbD6kUmD1bqyYxDKM52FEzhsnIkZSqdeECObgpU0gA5fp14J9/tG1dzrlzh6K816+nmwwA6NuX2hiGMQrYUTOGiYcH0LMnPf7nHwoyGzmSns+cqT8R4PPnk0Totm1UcMTMjOxnGMZoYEfNGC7TpwOXLgHz5tHziRMBW1vKsd63T6um5YiAAGD3bnqckED7AQOA0qW1ZhLDMJqHHTVjuJQvD3h7q54XKwaMGUOPZ80iYRRdZsECWk9v0QK4fJlqbU+erG2rGIbRMOyoGeMgIgKIiQHGjaO86gcPgN691UtG6hLPn6tKdFpY0P6bb3g0zTBGCDtqxvBZvBjw9CRtbAcHYMMG0sbevh348kuqQqVrzJhBI/6mTUkuFKCpe4ZhjA521IzhU7w4EB9PDjsighS9Dhyg3Oq//yat7NhYbVup4tkzlURoiRLksD/9FKhTR7t2MQyjFdhRM4ZPt25UrzoqCli4kNo6dACOHKGc61OngNatgeBg7dqpoGxZSiubNg04eJDaJk3Srk0Mw2gNdtSM4SOVknwoQIUt3ryhxy1bkoZ20aLAtWtUaev6da2ZqUbjxqRGFh9PI+nWrbVtEcMwWoIdNWMcdOgANGlC69GKdC0A8PIiQZQqVciBN2umCuLSNKmpwOvX9Dg+Hli2jB5PnswVshjGiGFHzRgHEgng60uP160jHXAFFSoAV64AnTpR1a0+fYBRo1S5y5pi40ayZeFCsjE8nEp2fvmlZu1gGEanYEfNGA/NmgHt21PE97Vr6sfs7ID9+0kkBaDRbP36JI6iCaKjSXEsMZGm6hcsoPbJk8lehmGMFnbUjHGxbBmNpnv1Sn9MKqVp8cOHAVdX4OFDoGFD4KefCl8cZdIkICSERFpMTSmwzdNTVaKTYRijhR01Y1yUK0cOMCvatwfu3QO6dCFBlMmTgebNgUePCsemM2eA1avp8fLlwKJF9HjaNAooYxjGqGFHzRgvFy7QWnBGODsDe/fScVtb0gyvXZvWuQtSzSw+HvjuO3o8eDDpe/NommGYNLCjZoyTa9dozXr48MxHyhIJFcG4fx9o144Czb7/niLFP17jziszZlB1rBIlgLlzVQFvPJpmGOY/2FEzxkmDBjTFnZwMDByYddnLkiVp3XrzZsq5vnULaNSIXvfuXf7scHMjLe81a4CdO3k0zTBMOthRM8aJRAKsXAnY2NAU+Jo12ffv04cCzHr3pqpW69YBFStSgFpSUt7smDgRCAqitKz586mNR9MMw6RBbxx1REQEevXqBTs7Ozg4OGDAgAGIzUKf+fnz55BIJBluuxU1foEMj+/YsUMTb4nRNqVKqZzjxIk0xZ0dbm7Ali3A+fMkSxoZSTnXZctS0Y/4+OzPIZMBHz6ont+9SyP8kBBy2DyaZhgmDRIhhNC2ETmhffv2CA4OxurVq5GSkoL+/fujQYMG2LZtW4b9ZTIZ3n00LblmzRosWrQIwcHBKFKkCABy1Bs3bkS7du2U/RwcHGBpaZlj26Kjo2Fvb4+oqCjY2dnl4d0xWkNR8OL0aSohee0aBZLlhNRUYO1akidVyJK6uACDBlHEeN26GSuKTZkC7NpFedvHj1NUuVxOtbP/+gtwdy+gN8cwjK6SG7+hF47a398fVatWxfXr11G/fn0AwNGjR9GhQwe8fv0aHh4eOTpPnTp1ULduXaxfv17ZJpFIsG/fPnTp0iXP9rGj1nPev6cAsadPgd9+A0aPzt3rk5Jo/drXl+pIKyheHPj8c6BGDVp39vSkAiDjxtHxsmWpUhZAQWvLl6tqTzMMY9AYnKPesGEDxo8fjw9ppgtTU1NhaWmJ3bt344svvsj2HH5+fqhfvz4uXryIxo0bK9slEgk8PDyQlJSEsmXLYsiQIejfvz8kWWgrJyUlISnNmmR0dDQ8PT3ZUesz/v7A2bPAkCF5P0dKCo2I9+wBjh4F4uKyf42pKd0cDBvGet4MY0TkxlHrhTZhSEgIXFxc1NpMTU3h6OiIkJCQHJ1j/fr1qFKlipqTBoC5c+eiVatWsLa2xvHjxzFs2DDExsZi1KhRmZ7L19cXc+bMyf0bYXSXKlVoUxAXR4FmucHMDOjenbbERBo9HztGo2x/fyAwkILQ7O2BHj1oqrtFC1orZxiGyQStOuopU6ZgoaI+cCb4+/vn+zoJCQnYtm0bZsyYke5Y2rY6deogLi4OixYtytJRT506FeMU05dQjagZAyEujhyotzfw669509q2tKSKXR060Ai7Vy9y0s2bA3//TdriDMMwOUCrjnr8+PHo169fln3Kli0LNzc3hIWFqbWnpqYiIiICbm5u2V5nz549iI+PR58+fbLt6+XlhXnz5iEpKQkWmawXWlhYZHqMMQD+/Rfw86Pt6VNgx468O1a5nKa2k5OBrl2BrVvJiTMMw+QQrTpqZ2dnOOcgwtbb2xuRkZHw8/NDvXr1AACnTp2CXC6Hl5dXtq9fv349Pv/88xxd6/bt2yhatCg7YmOmc2daa/72W+DIEapjvW0bBYXlFCFozVkqBQ4epECx778HTEwKz26GYQwSvcijrlKlCtq1a4eBAwfi2rVruHjxIkaMGIHu3bsrI77fvHmDypUr49pH0o6BgYE4d+4cvlPoKafh77//xrp163D//n0EBgZi5cqVmD9/PkaOHKmR98XoMF27AufOUd70/ftAzZpUFzq7spdRUZST3a2bqs3RkaRC2UkzDJMH9CKYDAC2bt2KESNGoHXr1pBKpfjyyy+xdOlS5fGUlBQEBAQg/iPBiQ0bNqBEiRJo06ZNunOamZlh+fLlGDt2LIQQKF++PH755RcMHDiw0N8PowfUr0951ePH0zrz3r3kdNeupePBwRQkFh8PXLkCXL4MXL2qiva+cYPOwTAMkw/0Ij1L1+E8aiPgwQNSMZs3j/KfAWDTpoxVxCpXBn7+mbTEOeWKYZgMMLj0LIbROtWqUSBYWkxMSOvb1JQEUxo1okjxatVobZphGKYA4BF1AcAjaoZhGCY35MZv8G0/wzAMw+gw7KgZhmEYRodhR80wDMMwOgw7aoZhGIbRYdhRMwzDMIwOw46aYRiGYXQYdtQMwzAMo8Owo2YYhmEYHYYdNcMwDMPoMOyoGYZhGEaHYUfNMAzDMDoMO2qGYRiG0WG4elYBoKhrEh0drWVLGIZhGH1A4S9yUheLHXUBEBMTAwDw9PTUsiUMwzCMPhETEwN7e/ss+3CZywJALpfj7du3sLW1hUQiUbZHR0fD09MTr1694vKXOYA/r5zDn1Xu4M8r5/BnlTvy+nkJIRATEwMPDw9Is6lfzyPqAkAqlaJEiRKZHrezs+MvfC7gzyvn8GeVO/jzyjn8WeWOvHxe2Y2kFXAwGcMwDMPoMOyoGYZhGEaHYUddiFhYWGDWrFmwsLDQtil6AX9eOYc/q9zBn1fO4c8qd2ji8+JgMoZhGIbRYXhEzTAMwzA6DDtqhmEYhtFh2FEzDMMwjA7DjjqfLF++HKVLl4alpSW8vLxw7dq1LPvv3r0blStXhqWlJWrUqIHDhw9ryFLdIDef19q1a9GsWTMULVoURYsWhY+PT7afryGR2++Wgh07dkAikaBLly6Fa6COkdvPKzIyEsOHD4e7uzssLCxQsWJFo/l/zO1n9dtvv6FSpUqwsrKCp6cnxo4di8TERA1Zqz3OnTuHTp06wcPDAxKJBPv378/2NWfOnEHdunVhYWGB8uXLY9OmTfk3RDB5ZseOHcLc3Fxs2LBBPHjwQAwcOFA4ODiI0NDQDPtfvHhRmJiYiJ9++kk8fPhQTJ8+XZiZmYl79+5p2HLtkNvPq2fPnmL58uXi1q1bwt/fX/Tr10/Y29uL169fa9hyzZPbz0pBUFCQKF68uGjWrJno3LmzZozVAXL7eSUlJYn69euLDh06iAsXLoigoCBx5swZcfv2bQ1brnly+1lt3bpVWFhYiK1bt4qgoCBx7Ngx4e7uLsaOHathyzXP4cOHxbRp08TevXsFALFv374s+z979kxYW1uLcePGiYcPH4ply5YJExMTcfTo0XzZwY46HzRs2FAMHz5c+VwmkwkPDw/h6+ubYf9vvvlGfPbZZ2ptXl5eYvDgwYVqp66Q28/rY1JTU4Wtra3YvHlzYZmoM+Tls0pNTRWNGzcW69atE3379jUqR53bz2vlypWibNmyIjk5WVMm6gy5/ayGDx8uWrVqpdY2btw40aRJk0K1U9fIiaOeNGmSqFatmlpbt27dRNu2bfN1bZ76ziPJycnw8/ODj4+Psk0qlcLHxweXL1/O8DWXL19W6w8Abdu2zbS/IZGXz+tj4uPjkZKSAkdHx8IyUyfI62c1d+5cuLi4YMCAAZowU2fIy+d18OBBeHt7Y/jw4XB1dUX16tUxf/58yGQyTZmtFfLyWTVu3Bh+fn7K6fFnz57h8OHD6NChg0Zs1icK6zeetb7zSHh4OGQyGVxdXdXaXV1d8ejRowxfExISkmH/kJCQQrNTV8jL5/UxkydPhoeHR7p/BEMjL5/VhQsXsH79ety+fVsDFuoWefm8nj17hlOnTqFXr144fPgwAgMDMWzYMKSkpGDWrFmaMFsr5OWz6tmzJ8LDw9G0aVMIIZCamoohQ4bg+++/14TJekVmv/HR0dFISEiAlZVVns7LI2pGL1iwYAF27NiBffv2wdLSUtvm6BQxMTHo3bs31q5dCycnJ22boxfI5XK4uLhgzZo1qFevHrp164Zp06Zh1apV2jZN5zhz5gzmz5+PFStW4ObNm9i7dy8OHTqEefPmads0o4FH1HnEyckJJiYmCA0NVWsPDQ2Fm5tbhq9xc3PLVX9DIi+fl4LFixdjwYIF+Pfff1GzZs3CNFMnyO1n9fTpUzx//hydOnVStsnlcgCAqakpAgICUK5cucI1Wovk5bvl7u4OMzMzmJiYKNuqVKmCkJAQJCcnw9zcvFBt1hZ5+axmzJiB3r1747vvvgMA1KhRA3FxcRg0aBCmTZuWbYlGYyKz33g7O7s8j6YBHlHnGXNzc9SrVw8nT55Utsnlcpw8eRLe3t4Zvsbb21utPwCcOHEi0/6GRF4+LwD46aefMG/ePBz9f3v3F9JU/8cB/L1lZ1tlWaIxYWYqMzUrJMyyslAUwYJCgi7MriREKYJCEJqGlvRH0GFJFyl1UZRhQaFlEiELdyFTDG1auoo06kJ0JpF/Pr+bX+Pp0cces2ce8/2CczG/53vO9/th7L2dP56GBmzdutUbQ513s63Vhg0b0NHRgba2Ns+yf/9+7N27F21tbTCZTN4cvtf9ynsrISEBr1+/9nyhAYDu7m4YjcY/NqSBX6vV6OjolDD+/gVH+B+of/CffcbP6VK0Re727dui0+mkpqZGOjs7JTs7W/z8/OTjx48iIpKZmSn5+fme9W02m/j4+MilS5ekq6tLLBbLors9azb1Ki0tFUVRpLa2VgYGBjyL2+2eryl4zWxr9XeL7arv2dbr3bt34uvrK7m5ueJ0OuXhw4cSGBgoxcXF8zUFr5ltrSwWi/j6+sqtW7ekt7dXnjx5ImFhYXLo0KH5moLXuN1ucTgc4nA4BICUlZWJw+GQt2/fiohIfn6+ZGZmetb/fnvWqVOnpKurSyorK3l7lhpYrVYJDg4WRVEkLi5OWlpaPG2JiYmSlZX1w/p37twRs9ksiqJIdHS0PHr0yMsjnl+zqde6desEwJTFYrF4f+DzYLbvrb9abEEtMvt6vXjxQrZt2yY6nU5CQ0OlpKRExsfHvTzq+TGbWo2NjUlhYaGEhYWJXq8Xk8kkOTk5Mjg46P2Be9mzZ8+m/Qz6Xp+srCxJTEyc0mfLli2iKIqEhoZKdXX1nMfBp2cRERGpGM9RExERqRiDmoiISMUY1ERERCrGoCYiIlIxBjUREZGKMaiJiIhUjEFNRESkYgxqIiIiFWNQE9FvV1NTAz8/vzlvR6PR4P79+3PeDtFCxqAmogVrz549OHHixHwPg+g/xaAmIiJSMQY10SJVW1uLmJgYGAwG+Pv7Izk5GV++fPG0X79+HdHR0dDpdDAajcjNzfW0lZWVISYmBsuXL4fJZEJOTg5GRkZm3N+DBw8QGxsLvV6P0NBQFBUVYXx83NPe09OD3bt3Q6/XIyoqCo2NjTNu7+jRo3j+/DnKy8uh0Wig0Wjgcrl+rRhEKuYz3wMgIu8bGBjA4cOHceHCBRw4cAButxvNzc2e5wtfvXoVJ0+eRGlpKdLS0jA0NASbzebpr9VqUVFRgfXr16O3txc5OTk4ffo0rly5Mu3+mpubceTIEVRUVGDXrl148+YNsrOzAQAWiwWTk5M4ePAg1q5dC7vdjqGhoZ8e0i4vL0d3dzc2btyIs2fPAgACAgJ+Q3WIVGbOz98iogWntbVVAIjL5Zq2PSgoSAoKCv719u7evSv+/v6e19XV1bJq1SrP66SkJDl37twPfW7evClGo1FERB4/fiw+Pj7y4cMHT3t9fb0AkLq6un/cb2Jiohw/fvxfj5NoIeIvaqJFaPPmzUhKSkJMTAxSU1ORkpKCjIwMrF69Gp8+fUJ/fz+SkpL+sf/Tp09x/vx5vHr1CsPDwxgfH8fXr18xOjqKZcuWTVm/vb0dNpsNJSUlnr9NTEx4+nR1dcFkMiEoKMjTvn379t87aaIFiueoiRahJUuWoLGxEfX19YiKioLVakVERAT6+vpgMBhm7OtyuZCeno5Nmzbh3r17aG1tRWVlJQDg27dv0/YZGRlBUVER2traPEtHRwd6enqg1+t/+/yI/iQMaqJFSqPRICEhAUVFRXA4HFAUBXV1dfD19UVISAiampqm7dfa2orJyUlcvnwZ8fHxMJvN6O/vn3FfsbGxcDqdCA8Pn7JotVpERkbi/fv3GBgY8PRpaWn56RwURcHExMTsJk60wPDQN9EiZLfb0dTUhJSUFAQGBsJut+Pz58+IjIwEABQWFuLYsWMIDAxEWloa3G43bDYb8vLyEB4ejrGxMVitVuzbtw82mw1VVVUz7u/MmTNIT09HcHAwMjIyoNVq0d7ejpcvX6K4uBjJyckwm83IysrCxYsXMTw8jIKCgp/OIyQkBHa7HS6XCytWrMCaNWug1fL3B/1h5vskORF5X2dnp6SmpkpAQIDodDoxm81itVp/WKeqqkoiIiJk6dKlYjQaJS8vz9NWVlYmRqNRDAaDpKamyo0bNwSADA4OisjUi8lERBoaGmTHjh1iMBhk5cqVEhcXJ9euXfO0O51O2blzpyiKImazWRoaGn56MZnT6ZT4+HgxGAwCQPr6+uZaGiLV0Yj8/34MIiIiUh0eIyIiIlIxBjUREZGKMaiJiIhUjEFNRESkYgxqIiIiFWNQExERqRiDmoiISMUY1ERERCrGoCYiIlIxBjUREZGKMaiJiIhUjEFNRESkYv8DaymSxeTbUbgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0056342807947658\n",
      "Epoch 200/1000, Loss: 0.0009460448054596782\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.005433894043865924\n",
      "Epoch 300/1000, Loss: 0.00040980230551213026\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0039557587103142096\n",
      "Epoch 400/1000, Loss: 0.00021682324586436152\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0023922913775701695\n",
      "Epoch 500/1000, Loss: 0.00010523070523049682\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0010976505412448507\n",
      "Epoch 600/1000, Loss: 5.629103907267563e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0005879542513866909\n",
      "Epoch 700/1000, Loss: 0.00011325538071105257\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0006749724416295066\n",
      "Epoch 800/1000, Loss: 3.578124596970156e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0005134563543833792\n",
      "Epoch 900/1000, Loss: 5.232938565313816e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0005037291800059999\n",
      "Epoch 1000/1000, Loss: 0.00016196101205423474\n"
     ]
    },
    {
     "data": {
      "image/png": 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fH3379tXVvm7SpAl+/sjM2HPnzgEADh8+jODgYGzbtk33mK+vLwIDA3Ho0CHs3r07Q+1L73hHjx7F/fv3cfToUaxZswarV6/O0meQKYJlW0REhAAgIiIiDPvCw4YJAQjRoIHQAMJrmExgKoRsqkyce3BDNGlCD9vaCvG//wnh4SGEv79hm8hYXhQXFydu3rwp4uLiPnwwOjrty/v7p7dvbGzG9s2EyMhIYWlpKbZs2aLbFh4eLmxsbMSIESN024oWLSrat2+f4rndunUTn332WYptY8aMEeXLl9fdByC2b9+eYh97e3uxatUqIYQQDx8+FADE8uXLdY/fuHFDABC3bt0SQgjRtWtX0apVqxTH6Ny5s7C3t0/zfWmPe+nSpRTbe/XqJVxdXUVCQkKK7RltZ2rHK1q0qEhOTtZt++qrr0Tnzp1TbVd6fyeZiRt8Rm3KtMXQT5+GrGxZbNosUFzuBAGB0ccH47//BD77DIiJAUaNAp4/B1q0AK5elbbZjOVp+fKlfenQIeW+Li5p79uyZcp9ixVLfb9MePDgAZKSklC7dm3dNnt7e12X8Ltq1qyZ4v6tW7dQv379FNvq16+Pu3fvQq1WZ6odlStX1t12d3cHALx48UL3OnXq1Emxv7e3d6aO/65KlSpBqZ20kwMqVKgAhUKhu+/u7q5re27hQG3KypcHGjUCNBqgRAlUegEc3ekEG0sbnHh8Av/e/Ru7dgGtW9MuAPDmDVXcCgyUtumMMeNma2ub6efIZDKI97ItpTZ+a2lpmeI5AKDRfknlsNTeR0bbmZp32649Vm61XYsDtakbMQLo3RsYNw6wtUXRgHuY4tkDADDy4EgkIALbtwM9euif8uIF1bJ+8ECaJjOWp0VHp33ZujXlvi9epL3vvn0p9330KPX9MqFEiRKwtLTE+fPnddsiIiJw586djz7Xy8sLp7UTXt46ffo0ypQpozvDLFiwIIKDg3WP3717F7GxsZlqo5eXF/z9/VNsO3v2bLrP0Z4xZ/TM/mPtzOzxchtnJjN1X3xBFwDo3h346y/02h+CaeVt8DruNUYdGIXl7ZZj9WrAzU2X2AzPngGffgqcOAEUKSJZ6xnLezJzJppb+6Yhf/786NWrF8aMGQMnJye4uLhgypQpkMvlujPbtIwaNQq1atXC9OnT0blzZ/j5+eHPP//EokWLdPt8+umn+PPPP+Ht7Q21Wo2xY8d+cAb6Md9++y3q16+PefPmoV27djhw4AD279+f7nNcXFxgbW2N/fv3o3DhwrCysoJ9OstdPtbOzB4vt/EZdV4yaBAAwGXLHhTP7wkAWHl5Ja6/uA65HJgzB/jtN/3ujx9zmlHGzM1vv/0Gb29vtGnTBj4+Pqhfvz68vLxgZWWV7vOqV6+OzZs3Y+PGjahYsSImT56Mn376Cb1799bt8+uvv8LT0xMNGjRAt27dMHr0aNjY2GSqfXXr1sWyZcvwxx9/oEqVKjh48CAmTpyY7nMsLCwwf/58LF26FB4eHmjXrl26+3+snZk9Xm6Tifc76lmmRUZGwt7eHhEREbCzs5OmEefOAX//TWuxLl/GxZnDUSNxAQCgqltVXBxwUfeLedMmoGdPIDERqFoV2LOHqm4xxjImPj4eDx8+RPHixT8a4IxdTEwMChUqhF9//RV9+/aVujl5Snp/J5mJG3xGnVeMGQMsXAiUKgUAqL5sN7pW6AoAuBxyGRuvb9Tt2rkzcPQo4OwMXL4M1KlD15zBjLG879KlS9iwYQPu37+Pixcvonv37gAg+VkjSxsH6rxCO1vsxg1KRfbwIf5Qfg4rC/oVN2TPEEQn6iee1KsH+PsD5coBQUFA7dp0dv3ypQRtZ4wZlDaZiY+PD2JiYnDy5Ek4OztL3SyWBg7UeUXHjpQz9NYt3frLgut34OcmNAgdnhCOyUcmp3hKiRLAmTMUtJOSaFJp9eocrBnLy6pVq4aAgABER0fj9evXOHToECpVqiR1s1g6TC5QL1y4EMWKFYOVlRXq1KmjS/WWmsaNG0Mmk31wad26tW6f3r17f/B4ixYtDPFWcpaDA9C2Ld2Wv/1n3bUL31b4BkXsaFr3gvMLcP/1/RRPc3QEjhwB2rSh+0FBQKVKHKwZY8xYmFSg3rRpE0aOHIkpU6bg4sWLqFKlCpo3b55mVpht27YhODhYd7l+/ToUCgW++uqrFPu1aNEixX4bNmwwxNvJedrub19foGRJIC4Olnv24USfE2havCmSNckYdXDUB09TqYCdO4H+/el+aCh1iYeEGLDtjDHGUmVSgfq3335D//790adPH5QvXx5LliyBjY0NVq5cmer+Tk5OcHNz010OHToEGxubDwK1SqVKsZ+jo6Mh3k7Oa9ECKFCAIq02ReDGjSjqUBTzW86HQqbAzsCdOHj/4AdPlcuBv/4CJr/tHX/9Wj9+zRhLHS+aYenJqb8PkwnUiYmJCAgIgI+Pj26bXC6Hj4/PByXS0rJixQp06dLlg5Ryx44dg4uLC8qWLYvBgwd/tKSa0VIqgS5daK1V9eq0bf9+4PVrlC9YHoNrUm7wfrv6IUmderq8adMAbeW6iAigUyeqwsUY09Mmx8hs1i1mXrR/H5lN+vI+k8lMFhYWBrVaDVdX1xTbXV1dcfv27Y8+/9y5c7h+/TpWrFiRYnuLFi3w5Zdfonjx4rh//z5+/PFHtGzZEn5+fikSr78rISEBCe9Er8jIyCy8o1wycyYVoVYogH/+oQoc27YB/fqhgE0BAMDTyKf4w/8PjK43OtVDjBwJ2NgAw4cDfn5A+/aU+TCTeQsYy7MUCgUcHBx0w242NjYfzezFzIcQArGxsXjx4gUcHBzSjCUZZTKBOrtWrFiBSpUqpagaAwBdunTR3a5UqRIqV66MkiVL4tixY2jatGmqx5o1axamTZuWq+3NsncXznftSoF6wwagXz+M8h6F38/+joiECEw8MhE9q/SEi61LqocZNIiGudu3p5PyRo2AZctoCRdjDHBzcwOAXK+cxEyXg4OD7u8kO0wmUDs7O0OhUCA0NDTF9tDQ0I9+EDExMdi4cSN++umnj75OiRIl4OzsjHv37qUZqMePH4+RI0fq7kdGRsLT0zMD78KAkpMBbSm5o0eB4GDkd3fH781/R59dfZCgTsCIfSOwoWPaE+c++ww4eJBWe124ANSqRYf65BMDvQfGjJhMJoO7uztcXFwyXHmJmQ9LS8tsn0lrmUygViqVqFGjBnx9fdG+fXsAVBbN19cXw4YNS/e5W7ZsQUJCAr7++uuPvk5QUBBevXqlq5GaGpVKBZVKlan2G1RYGODlRYPMNWtSlN2yBfj2W/Ss2hNzz8zFzbCb2HhjI773/h61C9VO81D161PPeYsWFPsbNwZ27QJatTLc22HMmCkUihz7QmYsNSYzmQwARo4ciWXLlmHNmjW4desWBg8ejJiYGPTp0wcA0LNnT4wfP/6D561YsQLt27dHgQIFUmyPjo7GmDFjcPbsWTx69Ai+vr5o164dSpUqhebNmxvkPeUKZ2cqlZWUBGgLwr9dciaXybGinX6cvveO3tCI9Gup+vhQYhSlElCrac31unW51nrGGGPvMKlA3blzZ8ybNw+TJ09G1apVcfnyZezfv183wezJkycpaowCQGBgIE6dOpVqsnmFQoGrV6/i888/R5kyZdC3b1/UqFEDJ0+eNO4z5ozo2JGuX7ygtVdnzwIPHwIA6haui47l6fFbYbew6tKqjx6udm3KB25tDQgBfP01pRZnjDGWu7h6Vg4wiupZ77txA6hYkU6D69QBTp6kdVdvx9afRT5Dy3Utce3FNRS0KYg7w+/Awcrho4d98oQyl2knus+dC4xOffI4Y4yxNHD1LAaUL08ZSxITgWLFaNvOnbqHC9kVQsCAAHg5e+Fl7EtMOTolQ4ctUgS4fx8oWJDuT5wI7N2bw21njDGmw4E6r5LJ9N3f2pnyp04B7yRzsVRYYn7L+QCABecW4Gro1Qwd2tkZePCAxq4TEoB27YC1a3O09Ywxxt7iQJ2XaQP18eNAhQqARgPs2ZNilwZFGqCEYwkICPTd1TfDKe/y5aMz6W7daDZ4jx60bEutzuk3wRhj5o0DdV5WuTJlKjt9GvjiC9q2a1eKXZQKJdzy0Tr0C88vYO3VjJ8aW1pS8rNvvqH7p09Tj3tcXI60njHGGHgyWY4wyslk79NmLLG1pXXWVla6h66/uI4qS6pAIzRwsHLAoxGPYG9ln6nDf/21fsmWiwvNEE9nKTpjjJk1nkzGPlSjBhXriImh9GLvqOhSEYNqDgIAhMeHY+KRiZk+/Nq1wA8/0O0XLyj96Pnz2W41Y4yZPQ7U5uD4caBvX6BKFbr/zuxvrZ+b/Aw7Ff2qW3h+YYYnlr3rl18oH7hMRt3fdesC69dnq+WMMWb2OFCbg127gFWrKFMZAPz3H00se4ejtSPm+MwBAAgIDPhvQJZqqfbrR78LVCp6iV69gB07svsGGGPMfHGgNgeff07Xly7RGPXz50BAwAe79aveDxUKVgAA+D/zx4braRfsSE+DBsCdOzSxLDkZ6NABWL48y61njDGzxoHaHNSvDzg60hrqWrVo23uzvwFAIVdgy1dbMKHBBADA6IOjEZUQlaWXLFIEuHKFzrA1GqB/f6rClZyc5XfBGGNmiQO1ObCw0Je7srWl61TGqQHAq6AXJjWchJKOJREcHYzpJ6Zn62X/+gvQ1knZvx8oU0affpQxxtjHcaA2F23b0vWdO4BCAVy7Bjx+nOquKgsVfm/+OwDgV79fcTvsdpZfViajpdw9e9L9hw8BT0/g5s0sH5IxxswKB2pz0aIFneLevauf/X3gQJq7xybHAgA0QoPBuwdnaWLZu9asAaZNo9uRkdSErVuzdUjGGDMLHKjNhb090Lgx4OUFVKtG2/bvT3P3L8p9gZKOJQEAxx4fw/bb27PdhMmTgW3b6PdCcjJlOJ00KduHZYyxPI0DtTnZsYP6nAdRchMcPqxfsvUeS4UllrZZqrs/fN9wxCVlPzfoF18AV68C2kQ8P/8MLF6c7cMylnXBwZQAoH9/YMKElI9VrgwULUpLF7Zs4fy4TBIcqM2JdiJZ9epUAisqCvDzS3P3piWa4otylCP8edRzzDk9J0ea4eVFda3LlqX7Q4YAw4en+ZuBsdwRFAQMGwYULw4MGEBrCDdvTrnPkyd02bYN6NSJ8uP26EG/NhkzEA7U5igxEWjYkG6n0/0NAPNbzoeVBeUFn3FyBh6Hpz4BLbPs7enkXjtu/eefNG59+HCOHJ6xtL18CQweTHluFy6kWq21atHyhDlzqBzsb78BY8YAjRpR+t1y5QAHByA6mvLlVq9O+zJmAByozc26dUCBAnQ2AaQ7oQwACtsVxs9NfgYAJGmS8N3+73KsKXI5jVvv2kUn+7duAc2aUXc4Y7lmzBhgyRL9D1ZfX+DsWaBJEwrQDRoAo0YB8+bRH2dAAHD7NhAerj+GWk2Jg+LjJXsbzHxw9awcYBLVs7T8/SkJd758dHYAACEhgKtrmk9JUiehxboWOPrwKAQEjvQ8gibFm+Ros86dAz79lGqGALTse9s2SkXKWI4KC6NC6j/+SBMsT50CRo+m/xsAoFQCbdoAxYpRCThXVxrHvnKFLrdv6wuvFyhAefS7dKHxbIVCqnfFTEym4oZg2RYRESEAiIiICKmb8nFqtRCurkIAQpQuTdd//52hpw7dM1RgKkTFRRVFkjopx5sWFiZE8eLUJECIokWFePo0x1+GmZu4OCFWrfpwe3KyEFOmCCGX0x+clZUQI0YIERSU/vFCQ4WYMUMIT0/9H6tcLkT9+kLExOTCG2B5UWbiBnd9mxu5HGjenG47OdH1R8aptX5q8hMKWBfA9RfXMf/s/BxvWoECQGAgdX8DlI+ldGlg9+4cfylmLpKS6Gy3Tx8qTKMVFERdONOm6avHPHwI/P47UKhQ+sd0caGz8QcPgO3bATc3Osbp00CpUsDTp7n6lpj54UBtjlq2pOsXL+j64MEPqmmlxsnaCa1KUyrSH4/8iOCo4BxvmqUl/W7Q1raOjwcGDuTvPpYFajXQuzely1WpgMKFabuvL1C1KnDiBA0BrV0LrF5NATczLCyA9u0piZA2819wMAXrVIreMJZVHKjNUbNmdGb98CF9UYWFARcvZuipDYo2AAAkqBMweM/gXGmeTEa1rXfsAKytac5OjRrA0aO58nIsLxKC1v2tX08BdetW4LPPgI0b6Yfqq1f0R3XpEtC9e/ZeK18+mnS2eDH9v0pMBOrV4yVcLMdwoDZHTk5AnTp0u3Rpus5g93e/av1QzY0ym+0M3IljD4/lQgNJu3bA9et08vPyJdC0KZ2s7NuXay/J8ooffqCKMHI5rXRo3RqYPx/o2pW6wzt10ndV55RBg6gYu0JBwbp2bfoDZiybOFCbqyFDgNmzKeMSkOHoJ5PJsL7Deshl9KfTY3sPJKlzL1NJiRL0fdqrF50k3b9PM8IHDuSSmSwN//xDS6sAyjj21VeUcWzECNo2bBiwYUPuLCn45BNg7176gZCQQOPgHKxZNnGgNldffw2MHUvXAC1NyWD9yXLO5TCm3hgAQFBUEGafmp1brQQA2NjQEOKaNbRyBqCTpTJlqPeesRS0fxRTpgDffAPMmEEl3ABapD9/PgXS3NKsGf3wrVRJ3xWkzVvAWBbwOuocYFLrqFNTujRw7x7w33+0fjQDEpITUPyP4giODoaF3AL3ht9DUYeiudxQ4NkzOqPWDv9ZWABLl9L3MWM6Z87Q8I429SdAAXr4cMO14c0bWqd99SplMjtzhhMDMJ3MxA0+ozZnr1/T+F2xYnQ/E/k7VRYqrO+wHjLIkKxJxsDdA7NdCjMjChUCLl+mkyWZjLq/+/YFvvsu11+aGbt3x0Lq1aOJYr160f0RIwwbpAHA0ZFyh6tUNFmTf02yLOJAbc7276eu7zt36H4mE203LtYYR3oegUqhwoH7B7Du2rpcaOSHZDJg6lRKKGVvT9sWL6aaCsxMrVkD1K8PPHpE94OCgM8/p2pXLVsCv/4qTbtKlKCiHwDNQF+6NP39GUsFB2pz1qwZRb0nT+j+jRuUTjQTGhdvjMmNJgMARuwbgZcxL3O4kWmrV49yTjRrRpNs+/enSWZbt9J9ZiaePaOz5XPngE2baBJXu3a0prlCBVqSJVVqT0tLGq+2osI2GDoUOH9emrYwk8WB2pw5O9MSEgAoUoSufX0zfZgx9cagTIEyeB3/Gt23ZXNNaiY5OdH34IwZ9Jvjr7+Ajh1pieyNGwZtCpPK8OFUstXbmwpuTJ5MXc0FCtC8C6nnjRQrRklVAErC0rZthiduMgaYYKBeuHAhihUrBisrK9SpUwfnzp1Lc9/Vq1dDJpOluFhpf9m+JYTA5MmT4e7uDmtra/j4+ODu3bu5/TaMhzZLmfZzyUKdSUuFJcoWoOLShx4cwo5bO3KocRkjl1NGxz179CW3r18HqlWjSoTa+gksD9q5k9J4WljQr7QzZ4C5c+mxFSv03c5S69CBunwAIDSU1lwzlkEmFag3bdqEkSNHYsqUKbh48SKqVKmC5s2b44U2FWYq7OzsEBwcrLs8fpyynvKcOXMwf/58LFmyBP7+/rC1tUXz5s0Rby7l67SB+tkzuvb1pQXLmbSq3SrYWNoAoLXVr+Ne51QLM6xlSypuVJZ+MyApiVageXvrh+FZHhIVRWuiATqTLloU6NmT/n5796bub2Myf74+jemGDcCWLdK2h5mOXC4QkqNq164thg4dqruvVquFh4eHmDVrVqr7r1q1Stjb26d5PI1GI9zc3MTcuXN128LDw4VKpRIbNmzIcLtMqnrW+5KThXB0pApAFhZ0HRiYpUOtvbJWYCoEpkL4/O0jNBpNDjc2Y6KihBgwQF/YCBBCqRRi+XJJmsNyy7ff0j9uyZJCxMbq/9GLFBEiPFzq1qXu6FGq0gXQ/zsuD2e28mT1rMTERAQEBMDHx0e3TS6Xw8fHB35+fmk+Lzo6GkWLFoWnpyfatWuHG+8MXD58+BAhISEpjmlvb486deqke8w8RaGghAyAvpswC+PUANC9cnc0K0Glrw4/OIw1V9bkRAszLV8+mlx77Jh+5VliIs0Mv3dPkiaxnJaYCJw8SbcXLwaOHKGub4Cy42iXAxibxo1pRnqtWrTOulevDBXEYebNZAJ1WFgY1Go1XF1dU2x3dXVFSBozlcuWLYuVK1di586dWLt2LTQaDerVq4egt1mCtM/LzDEBICEhAZGRkSkuJm3WLJohq11zmoVxaq31HdYjnzIfAGDQ7kF4GiFd2atGjYCbN6n7Wy6ngkYVKgDjxtEE4aTcy3zKcptSSdn0du2i6f8DB9L2774DmjSRtGkfVaAATS6zsaEfGH/+KXWLmJEzmUCdFd7e3ujZsyeqVq2KRo0aYdu2bShYsCCWZnMt46xZs2Bvb6+7eHp65lCLJVKqFJX40/YsHDmS5RlYBWwKYO0XNMM1QZ2AXjt6QSOkO2OwtqaU5jduUBnuxESqzOXtTWPZFy5I1jSWXZaWNIN67lyaY1G0qD5VqLErU0b/w/jHHzO9LJKZF5MJ1M7OzlAoFAgNDU2xPTQ0FG4ZrCNraWmJatWq4d7b/k/t8zJ7zPHjxyMiIkJ3eZpXiiXXqEFdhuHhlNUpi9qVa4clrZfASmGFo4+OYoH/gpxrYxaVK0fLuHbtAjw8qLfx4UNanTZsGBATI3ULWYYkJVEXd0IC3X/6lKb2AxSwra2la1tmPX9O1zEx+gLsjKXCZAK1UqlEjRo14PvO+KlGo4Gvry+8vb0zdAy1Wo1r167B3d0dAFC8eHG4ubmlOGZkZCT8/f3TPaZKpYKdnV2Ki8k7dIimTTs70/1sdH8DwMCaA/Frc8oGNc53HG6H3c5uC7NNJqMTsHv39D2lQgALFwIlSwK7d0vbPpYBK1bQP17DhvSPN348ZR/75BNaQG9Kfv9dX2Xmn3+oTBxjqTHA5LYcs3HjRqFSqcTq1avFzZs3xYABA4SDg4MICQkRQgjRo0cPMW7cON3+06ZNEwcOHBD3798XAQEBokuXLsLKykrcuHFDt8/s2bOFg4OD2Llzp7h69apo166dKF68uIiLi8twu0x61rfWtm00E9XFha6bNs32ITUajfD520dgKkSpP0qJJHVSDjQ05xw9qn+72ssXX9BEeGaEoqKEcHWlf6gFC4Tw86PbMpkQFy5I3bqsGT9e/8dXuTL/8ZmRzMQNkwrUQgixYMECUaRIEaFUKkXt2rXF2bNndY81atRI9OrVS3f/u+++0+3r6uoqWrVqJS5evJjieBqNRkyaNEm4uroKlUolmjZtKgIzuTwpTwTqN2+EUCj0XxpWVkLEx2f7sJOOTNIt2Rp9YHT225nDIiOF6NVL/7bt7en7nxmhadP0y7Hi4oSoU4fu9+4tdcuyLjJSiIIF9X+Af/4pdYuYgWQmbnCZyxxg8mUuterVA/z8KOViZCStb2rUKFuHTFQnouyfZfEo/BFkkMG/nz9qFaqVM+3NQfv20dyely9phvj33wPdu1NOjYYNpW4dw4sXND4RHU25u5OTqaCMrS1w9y7wdjjLJK1YAfTrR7ft7GhspmBBadvEch2XuWRZ89lndO3gQNdHjmT7kEqFEnu77YVCpoCAQJsNbRCXFJft4+a0li2BwED67tdoqNhS3br0O+Xrr2n1GpPQnDkUpGvWBL78ksqnATRGbcpBGqAsalWr0u3ISEpcz9g7OFAzvWaUrASv36b/zIFADQBeBb3wi88vAIAXMS8MXrgjoxwdaU7Pnj108qatwLVuHa1gmzNHP9mYGdCrV8CSJXR72jRKvXnvHlVkGTFC2rblBIWCkrZol5YtWkRLEhh7iwM106tdG8ifn85cAODs2RxbtzTSeyS8C9NM+u23t2PJ+SU5ctzc0KoVrbueORNQqWhbbCwlTilblgI5M6DISEpiUr06LYbXnnF+/z2locsL6tal3gEfH1qCNnmy1C1iRoTHqHNAnhmjBoBu3Si14ZUr1N+7fz99OeaA13GvUeKPEohIiIBSocT5/udR2bVyjhw7tzx9CkyYQGfa71q6FBgwQJo2ma3YWPp77NCBxnIfP9YP0+QVAQHUvQ9QLgNtlzjLc3iMmmXd+vU0s0pbVSuLeb9T42TthEM9DqFBkQZIVCei4+aOiEww7vSrnp7A338D164BLVrot3//PTB9OsUO/qlrINbWwM8/0+3hw/NekAaAR4+oZCdAZ9iMgQM1S8unn9J1Do1Ta9UqVAvbOm+Dp50n7r6+iz47+sAUOnUqVqTfLydP0ghBbCz1TpYuTePXs2ZR3g2Wg7TjDW9z82PvXjrLtLWlnN55UaVK+iId+/cDR49K2x5mFDhQs9SVK0fXFy9SV3gOcrZxxpavtkAhU2Db7W2YedJE8jODEmCdPUsrhIoWpSyQDx5Quubixensm4sh5ZBly2gGn48PfajTp9P2wYP1GfTymndzgANUQcYEfsiy3MWBmn1ozBgaJytQgL4kjh/P8Zeo6lYVdioal5l4dCKOP8r518gtMhnQuTNw+3bKCWehofQdW6UKnX3z92s2JCRQ7m6AxhmOHqVqWSoVMGqUtG3LbZMnU8ERgMq87dsnbXuY5DhQsw9VqEDX2rGyHO7+BgCVhQp7u+2FXEZ/gm3Wt8HzqOc5/jq5ycqKhhHv3wc6ddJvv36dZo43aaKfQM8yadMmqojl4UHrjLWFN/r1o0pveVmxYkD//vr706bxrz4zx4GafUg7Pv3yJV3nQqAGgLqedfG/5v8DAEQnRaPZP82QpDa9ItGFClFcOX4c8PLSb792jasXZokQVLACoNJmt24BBw9Syri8fjatNWEC/RIE6Kz60CFp28MkxYGafahIEZohpR1svXGD+nVzwfDaw9HBqwO9zMsbGLh7YK68jiE0bEir2mbPph7a169pbtDEiZSfo3dvynbJPuLUKZo0ZmVFa+C0XeCdOtFEAHPg4UG9B/K3X9F8Vm3WOFCz1DVtStfaSTu5dFYtk8nwzxf/oIRjCQDAqsursPTC0lx5LUOwtKSJyjduUEbW+HjKz1GxIrBmDZ1xf/MNTUBjafjjD7ru0YPGDjZtovtjxkjXJilMmEAzF1Uq4MwZngFuxjhQs9RpA7X2V/yxY7n2UtaW1jjS8whsLG0AAMP3DYffU79cez1DKFkSOHAA2LaNgrM29ahaDaxaRRnO+vWjZbPsHULQdPr8+YFvvwX+9z/60Hx8KDOZOXFzA2rV0hfs0M56Z2aHM5PlgDyVmUwrLCxlBZ8yZahqRS56EvEEI/aNwI7AHXDL54bz/c+jsF3hXH1NQ1CrKbPZlCnAkycpH7OwAAYOBP78U5q2Ga3YWFqYXqQI3T5wQJ+L3tw8fQqUKEEVw44f53JueQRnJmPZ5+xMkWXZMrp/5w4tGs5FReyL4J8v/0Ell0oIiQ5Bs3+aGWWlrcxSKGh8OjCQzqbfPTFMTqZ5Qu8HcLNnY0OFKmJjab2btrKbOdq0if5QAD6rNlMcqFnapk6lbrdq1eh+Lqynfl8+ZT5s6LABlnJL3Aq7hbYb2kKtUef66xqClRUF7AsXaMixSxfafucOdYVPmkTDkB07Uspns3PgAHDiBHV/x8cDCxbQ9h9+oMXr5srHR3/78GH6A2JmhQM1+7jGjek6F8ep31XWuSyqudOPA9+Hvuiz0zTSjGaUTAZ4ewMbNlBAbtSI4tLPP1M+8a1bKd9Mixb0keeht542IahkZaNGwNq1dBb54gV1fX/1ldStk1bVqkDr1vr7s2ZJ1hQmDQ7ULH3nz9N4NWCwQG0ht8DBrw/C084TAPDP1X8w8chEg7y2oVWvTmfR27fTijhtDWyATjCbNKEKiFu30lh3nnXsGI0N5MsHtGtHNZkBYNAgfZYuczZhgv72tm20tpyZDQ7ULH3ff6+v8WiAcWoteyt7nP7mNBxUDgCAmadmYvH5xQZ5bUOTyYD27em7d906Wsr1rnPnqDv83ZOqPOevv+i6WzdabH7uHAXovn2lbZex8PYG6tXT3//lF+nawgyOAzVLnzZLmaMjXRtgnFrL094Tx/sch1KhBAAM2TsEO27vMNjrG5qFBcWpK1eAXbuAOnVSPh4bq1/OlZCQh7KevXxJZ4kATYFf/PYHWceOgIuLdO0yNqNH62+vXcszEM0IB2qWviZN6FrbJ2ug7m+tyq6V8V+X/yADTSbq8m8XnA06a9A2GJpcDrRtC/j5UebM+vVp+8mTtD67Uycazy5alCanXbokaXOzb80a+vuqWZOWIa1fT9uHDJG2Xcbm88/pD8DSksZB5s2TukXMQHgddQ7Ik+uoteLjAQcHfcYOA6ynTs3aq2ux6Pwi+AX5oYB1AZzpewZlCpQxeDukIAT9Ppo1K+2Uzw0a0Fysdu30tVRMghA05f3uXer+jo2lWtOVKlHXgjnP9k7NrVvAw4c0DmJtDTx+nDLfATMZvI6a5Rwrq5RjYwYcp37X15W/xqEeh1DLoxZexb1Cs3+aISQ6r/T9pk8mo46NgwcpdvXu/eH8qpMnqae4eHEK6Cbz8/vZMzo7zJeP1qtpu70HD+YgnRovL6BlS8pYFhcHzJ8vdYuYAXCgZh+n7f52cKBrA45Tv8tWaYvd3XajcP7CeBzxGN4rvBGVECVJW6RSuTIlTXn8mGphly6d8vGgIJqQpu0AMXqFC9PZ9PnzdNHO/P76a6lbZrxkMn3e88WLKWCzPI0DNfs4baDWVtMy8Dj1u1xsXdCydEsAwKPwR2iwqgES1YkfeVbe4+5OtbADA6nY1DffUO0GgAqCeHrS42fO0MnX0qVAlLH+ppHLgXLl9EuyevSgXN8sdcnJ+gxlr17RxDKWp/EYdQ7I02PUAE30OX+ephl37CjZOLWWRmjQdkNb7L27FwDQqGgjHO11FDIz7yp99QpYvhxYuJDSQ78vXz6aVT5gAFCjhuHb94HAQKBYMfqF8eIFFfZOTgauXqUxapa2vn2BlSvpdvnywPXrPFRgYniMmuUspZKmHn/6KX0ZSDROrSWXybGryy40LtoYAHD88XF03NxRsvYYiwIFqMTmgweUIKV585SPR0fTfK2aNSlQa1NpS0IIWjxeuDCtmV63joJ0rVocpDPiu+/0t2/epAkMLM/iQM0yztGR0hkClJNZQgq5Aod6HkKdQrTYeNvtbRiwa4CkbTIWFhbAl18C+/fTBOGJE6li4rsuXqTvem3SOYM7dw64fZt+KXh50RItgGbKsY+rVEk/JAVQOVCWZ3GgZhkTHk7rWrVZNiQcp9aykFvg1DenUMmFzsCWXVqGRecWSdwq41KsGA1nPn1KSVTattX3kCYm0mS0jh2BHTtome60aQaqkb16NV136ADcv0/T2ZVKfaUS9nEjRuhvHzhAZ9YsT+Ix6hyQ58eoASApCXByov5TgNa+3r4tbZveSkxORJM1TXAm6AwAYGGrhRhSi5NlpOXZM+pp/ucfGtpMTePGNKerY0cgx/+k4+PpFD8iAvD1Bf77D/j9dyq+sXlzDr9YHqZWU4J47S+rAQNo1iAzCTxGzXKepSVl1dAKDDSaHJZKCyVOfXMKo70pxeLQvUMx+uDojzzLfBUqRJUjr12jE9nRowFX15T7HDtG85VcXYGuXamrPMfs3ElBukgRWqOvnbXcq1cOvogZUCiAYcP09//+W8KxDJabTC5QL1y4EMWKFYOVlRXq1KmDc+fOpbnvsmXL0KBBAzg6OsLR0RE+Pj4f7N+7d2/IZLIUlxYtWuT22zBN2jEx7a8/idZTp0Ymk2HOZ3Mwph6tL/3V71e0Wd8mT5XHzA2VKwNz51LX+M6d1P0tf+dbIT4e2LiRloBpxcVlM6GKdjy6Z0/qsg0Lo18E789+Yx/Xty/9P6xWjf6xli+XukUsF5hUoN60aRNGjhyJKVOm4OLFi6hSpQqaN2+OFy9epLr/sWPH0LVrVxw9ehR+fn7w9PREs2bN8OzZsxT7tWjRAsHBwbrLhg0bDPF2TI82UMfH07URBWqAgvUvPr/Au7A3AGDP3T2ouawmktXJErfM+FlaUpDeuZOSpsyeTWmltUaMABo2pNoZU6ZQBrRx44DLlzMZtF++pOAM0Bm0Nmj36GFiuU+NhIMD/cN8+y3dX7w4j9dDNVPChNSuXVsMHTpUd1+tVgsPDw8xa9asDD0/OTlZ5M+fX6xZs0a3rVevXqJdu3bZaldERIQAICIiIrJ1HKOXnCyEvb0Q9N0shJeX1C1KlUajEa3WthKYCoGpEEX+V0RExOXxf5tcoNEIcfy4EN26CWFhof9nt7TU3waEKFtWiEmThLh6lZ7zUdevC7FggRAvXugPfO1arr+fPC02VghHR/osd+yQujUsAzITN0zmjDoxMREBAQHw8fHRbZPL5fDx8YGfn1+GjhEbG4ukpCQ4OTml2H7s2DG4uLigbNmyGDx4MF69epWjbc8zFAr69a516xYQGipde9Igk8mwp/seDKoxCADwJOIJiv1RDI/DH0vcMtMik9E/97p1lLJ04kTA2ZnmFQLURS6X03SF6dOpG71x4wwcuEIFGlvdsIHWTteo8WERbpY5//wDxMTQ7YULpW0Ly3EmE6jDwsKgVqvh+t6sF1dXV4RkcFLT2LFj4eHhkSLYt2jRAn///Td8fX3xyy+/4Pjx42jZsiXU6XQfJSQkIDIyMsXFbDRuTH2iHh50X+L11OlZ3GYxZjWdBQB4E/8GZf8si8Aw6TKqmTIPDwrGT55QQqwaNSijrDarrJ0d9Vy/212u0QBTpwL+/vr9UvjnH7rmSWTZ5+qqL0V76BAlJWJ5hwHO8HPEs2fPBABx5syZFNvHjBkjateu/dHnz5o1Szg6OoorV66ku9/9+/cFAHH48OE095kyZYoA8MElz3d9C0Hd30II8e231M02ZIi07cmAdVfXCcU0hcBUCKdfnMTh+2n/27KM8/cXomdPIays9N3gKpUQnTsL8c8/Quzerd/u4SHE4CqnxcHGM0TiuUtCBAbSAwoFdYGz7ElOFqJIEf0HPmKE1C1iH5GZrm+TCdQJCQlCoVCI7du3p9jes2dP8fnnn6f73Llz5wp7e3tx/vz5DL2Ws7OzWLJkSZqPx8fHi4iICN3l6dOn5hOotbZtoy+EChWkbkmGBIYFilp/1RKYCiGfJhfTj08XarVa6mblCa9fCzF/vhCVKqUcuwaEKFBACKUy5TZ720TRpeJVcQlVhGjZUurm5x0zZ77zIdsLER0tdYtYOvLkGLVSqUSNGjXg6+ur26bRaODr6wtvb+80nzdnzhxMnz4d+/fvR82aNT/6OkFBQXj16hXc3d3T3EelUsHOzi7FxexoP/MbN6iggpErU6AMTvQ5gV5VekEjNJh0dBLK/FkG4fHhUjfN5Dk6AsOH05psf3+q2lW5Mj326hX1yNbABfTDMjjjJSJiLLHxeiXEwZqqhICSk929K+GbyAv69qXsbgCtU1+3Ttr2sJxjgB8OOWbjxo1CpVKJ1atXi5s3b4oBAwYIBwcHERISIoQQokePHmLcuHG6/WfPni2USqX4999/RXBwsO4SFRUlhBAiKipKjB49Wvj5+YmHDx+Kw4cPi+rVq4vSpUuL+Pj4DLfLbGZ9a23bJkT+/HQBhNiyReoWZZhGoxF9tvfRzQi3mWEjzgadlbpZedKTJ0IsXixEq1ZCrJd3FwIQv2M4zRxHvGgv2yGW/hEn7t4Von9/+lMqXVqI774T4uBBITLxX5Bpde+uP6uuXDmD0/CZFPJk17fWggULRJEiRYRSqRS1a9cWZ8/qv2QbNWokevXqpbtftGjRVMeSp0yZIoQQIjY2VjRr1kwULFhQWFpaiqJFi4r+/fvrAn9GmV2gvnQp5TqdYcOkblGm/XLqFyGbKhOYCiGbKhPzz86Xukl5V3S00NjaCgGIaS39hItN5Add5NbWQsjlKbfZ2grRtq0Qf/4pBI9SZNDp0yk/xOPHpW4RS0Nm4gbn+s4BZpHr+10aDa3TefOG7lesSPkoTcyxR8fQYm0LJKgTAABtSrfBlk5bYGVhJXHL8pgNG6iLu0QJIDAQmiLFcCnYFXu7rsWhIC/4++snLAO05MvSEkigf5YP0sqfPUslmM3hv1qmCUF508+fp8+9fXtg+3apW8VSkZm4wYE6B5hdoAboC2DnTv39Fy+AggUla05WBUcFo9ayWngWRdnq6nvWx4k+JyCXmcz0DePXpg2wZw8txP70U7o4OFCueJUKcXEUfI8epZjybqEQCwsK1MOH02Gcnak2TGIiULcu4ONDl9q1Kbizt27dol8zMhkN/r+7bo4ZBS7KwXKfNrNFvnx0bWTpRDPKPb87Ho54iNalWwMATj89jU5bOiE6MVriluUhn35K9ZO7dQPWr6dtHTsCKhUAwNqastP+9BN1zFy7Bvz4I6UpTU6m+YqDBgGFC1PpagsL2n7qFK3T/uQTCt5t2lCKUwb6oFq0oDPsBQukbg3LJg7ULGu0eb+1/ZNHj0rXlmyyVFhid7fd+KvNX7CUW2Lrra2otqQaBvw3AEGRQVI3z/SNHAlcvUpd3//+S9vezvZOTcWKwIwZNBP86lW6XbcunRw+fAi8m1/I3h6wsaHqq3v2pMzz8eIFxahr19JIuJKXXb9ONeQBYMUKmgXOTBZ3fecAs+z6fn+c2ssrTxSu93vqh6+2fKXrClcqlPiz5Z/oV70fZDKZxK0zcTt30pCJhwelOFMoMvX0V68APz/g9Gk6m/b316czBahq5mefUXERb2/g8GH974ECBahKa8OGQKNGQJUqmX5503LzJqVq1fr1V/rBxIwGj1EbmFkGaoDKKCUmUqklgMYc3y9sbILC48PRd2dfbLut70et5VELW77agqIORSVsmYkJCqIhkXbtaIika1eqmfn998Bvv2X78G/eAFu3Um/6sWMfVvFyc6Pfk+HhKSerAUD+/MB//1HQzrMaNdKn+C1aFLh3jyuUGREeo2aGMW0aMGsWULUq3T92TMrW5BgHKwds7bwVe7rugZ2K/gOdf34eJeeXxPjD45Gs4bKZGbJ+PfD11zQeHRtLkREAOnfOkcM7OgL9+gFHjtAJ+qpVQP/+NIcKoN+NL16kDNI2NjTpLCqKRm20Kf3nzgXq1KHfEP/+Czx/niNNlNYgKkoDuZyqquzYIWlzWNbxGXUOMNszaq3vv6clIQMHAkuWSN2aHBWZEIlv932LNVfW6LaVcy6Hc/3OIb8qv4QtMwE1agAXL9LfhLMzBeyiRWmgOZeHEd68AS5dopfXXgJTqceSLx9QqxYF+vv3Uz5WpAh1odetS3/a1ta52uScl5BAM/DCwuh+vXo0bsCMAnd9G5hZB+qXL2n865dfgDJlUv82zANuvbyFzv92xrUXtF7c084Ti1svRusyrSVumZG6dw8oXZoGgoODgaFDgS1bgDFjgDlzJGnS69e0DMzPjy7nztGZ9fusrCjGab8ZbWxoLpa211ibmbNWLaBUKTphNVpjx9LnLZPRG7p4EahWTepWMXCgNjizDtRffEFdatovgmfP9CUw86AtN7Zg7OGxeBj+EABQ3b06WpdujR8b/MiJUt41Ywatm/7sM1ocXbAgEBcHXLhAZ9pGQK2mOVdnz+onqL1/Vq3l6UlBuXRp+nPXpre3t6eAXbOm/lLUmKYx3L9PDdfq2xdYvly69jAdDtQGZtaB+o8/gO++ozRRkZF0upHO0pu8ICYxBlOOTcFvfr9BgP77OFk74bfmv6FH5R6cLAWgadVXr1JQyJ+fxqVLlKAzbSOePR8cTAE7IICKjFy9mrnx6iJFKCmYiwvd9/Wl9eDFi0v4tr/9ltasz5tH/fdBQbTwnEmKA7WBmXWgvnKFJpNZWtJamX79gGXLpG6VQZx/dh4dNnfA08inum1lC5TF7y1+R/OSzc13Odft2/rMJKGhNMNr2zZg3DiafGhiwsIoudfdu/Q749494NEjGmoPCUn9OSVKULa0rVvpv4WdHfU4ay9VqtBHpC12leuEoBe+coUC9qhRBnphlhYO1AZm1oH6/fXUJUvSN5mZSNYk4ze/3zDp6CQkqvXTi2u618TKditRybWShK2TyF9/0eyrVq1oOZaLCxAfT7O7tCsE8oj4eEqy4u+vH/++ffvDpWKpaddOPxFbCJq9XqmS/mw8xy1fTj+aSpSgXx1GPbie93GgNjCzDtTAh+PUT57QoJ4ZCYoMwuDdg7H77m7dtv7V+mNG0xkoaGt6OdCz7fFjShd29SoNhZQuTRMNzaCXISKCJqqdPUuX69fpv8T7FApKf1q7NlCoEI0gARSoK1WiDG3a6/LlaQQhy6KjgZUraTJfYiKlcWvVKhsHZNnFgdrAzD5Qa8ep7e3pW+rvv4EePaRulSQOPziMHw79gEshlwAAtpa2GFNvDCISIlC3cF108OoAhTwvp8R6j/ZH3MSJwPTpUrdGMrGxdBJ7/Tpl2z14EHj69OPP03p31ODNG0ryVr48dZ9nKIA/fkwD5dqv+1atKFgzyXCgNjCzD9Tvj1P36UO/3s2UEAL77u3D5KOTERAckOKxUk6lMKLOCPSu2hv5lPkkamEuSk7Wr2OKjKTTw4QEOrOuZIbDAGkQgkaIjh+nEYFLl+i/UWxs6vu7uFB6/dq16eMdMUL/WKFCFLC1l88+SznRW6d1a2DvXrrNVbUkx4HawMw+UGs0wNq19O3TuzdQrBjNtDFzQghsubkFIw+M1OUO17JT2aFftX4YVnsYijsWl6iFOUwICsZFiwJ//kn9vt26AeXK0TooM+j2zg61GnjwgIqIXL9O15cupb5kTCajgP1urnOtFSuAb76h2xcvUrbWMmWAslEXUGZef5S2eIR8yeGU+/vXX3P1PbG0caA2MLMP1FrR0VRnWK2mQF2smNQtMgpxSXGYd2YeZp2ahbjkuBSPySDDga8P4LOSn0nUuhx09SpNZ1apKBFO//7Apk3A+PHAzJlSt85kvX5NAff8ef2EtZcvP9zPxoZWXzVuTGPfpUrRvql99O54jtKKh5i+pzoaNqeUazEx9Js7W2PhLMNyNdf30XTKGS5dujSzh2N5iTYfI5Bn8n7nBGtLa0xqNAl3ht/BwBoDYSHTF0ZQyBV4HvUccUkUwI8/Oo57r0101ry2hGXLlhSs9+2j++3aSdemPMDJCfDxod87O3fSire7d4E1a4AhQ4Dq1ensOjaWKoxt3UpZfdu2TRmkbWwAe4sYAEAwPHBCXR+aA4d0j2/cSMvI3NyA+vVpmsnUqTTl5NSp1LO4McPI9Bm1SqXCt99+i5kzZ8LS0hIAEBYWhj59+uDUqVN4o12mY0b4jBq0TmXxYlqac/s20KsXsHq11K0ySo/DH2PmyZlYeXmlrsCHvcoencp3wr77+xAUGYR6nvXQu0pvfFXhKzhYOUjb4IwqXx64dYuGQQoWBJo3p2/9Z894KVAui42lrvLAQLrcvk3zx54/p8CurcddCEG4jCp4gJIYi1/QwuUSOvl9j+IlZJg2jQJzWo4c0ZehP3CAfjRok7kULUodaM7OPMKRUbna9X3mzBn07NkT+fLlw/r16/Hw4UP07dsXZcuWxd9//42iRpU/zzA4UIO6u52d9cXqixShrBD8vzZNTyKeYOWllVh9eTUeRzxOdR+lQokWpVqgS4UuaFu2rfFOQNPWP1YqKb/mjz8CixZR9/dff0ndOrOmVlPGtTNnaLZ5x/VfICkuGT9iJq6DJviVK0fzQYsVo8UbMhllfH32jMbIHz6kTjLt1/vEiZQl9n02NrTPxo1A5cq07d49en1PT5r49vb8zuzl+hh1dHQ0Bg0ahH///RcajQbTp0/HDz/8YLaZmDhQv9WuHbBrF509aTT0P7xECalbZfQ0QoPjj45jzZU1+O/Of3gd9zrV/fpV64dlnxtp1reffqL65G3a0N9AkSKUqnL3bpptzIyGSFYj8J4Ce77Zit1+Tjgpawi1SH3JoKMjnTG7ulIQtrWl66AgOmOPjKSpKfHxNMat9W5um3eDulxOnSyenvrLqFEUwAHqGbC0NI9gnpm4kaUq4nfu3MGFCxdQuHBhPH/+HIGBgYiNjYWtrW2WGszyiMaN6Uva3p4Wex47xoE6A+QyOZoUb4ImxZsgWZOMM0/P4L/A/7D11lZd8Q8AOPvsLFZeWokvvb5EYFggJh2dhDZl2qB16dYo6STxMhvt+HTHjvQtHRRE3+pNm0rbLvYBmYUC5coB5ZZ7YVSFCngDJ5xefhO337ji1i3qNr97lyasvXmjTzqYGdWqUfCtWJFSKzg50bVaTd3xz59TNjcAGD5c/7wZM2i9uKsrPf/di4cHLct3cMiRj8GkZPqMevbs2ZgyZQoGDBiAuXPn4t69e+jRowciIyOxdu1aeHt751ZbjRafUb916RLNbFEqKfvR118D//wjdatMlkZo4PvAF4svLMbO2zuhAQ00KhVKFLUviruv7+r2LVugLFqVboXPSnyGhkUbwlZpwB/NGg11c2/fTgH799/pDPvLL2lmEzNOjx8DLVpQZE6l/Gh0tD6n+atXdMYcE0NnvXI5zRdUqeh2SAj9Nnv6lDrSMpPMxcWFEteVLk0LBy5eTHvfdxeTTJ0KrFoFuLunvLi50XXjxjS/1Vjlate3u7s7Vq5ciZYtW+q2JSUl4ccff8T8+fORkJCQtVabMA7Ub72f97twYcqdaKZDIjnpWeQzrLmyBuuvrceNlzd022Wgz1ZbxQugQH5t8DWUKVDG4O0EQH2eV67QZMJevaRpA0vf69cU0bQLsR0dKdLa2OTI4SMigBs3aILbkyf0cq9e0SUsjM7Ww8JSXweeGpWKZrZXrkzzFAsUoDStN26k/ZxHj/Rj6j//DKxfT2fqrq7040B77eJCs+oN3SGcq4E6LCwMzs7OqT52/PhxNGrUKDOHyxM4UL+jfXuaDqodp757N400SSyrroVew/pr67Ht9jbceXUnxWNymRwWcgvMajoLnxT5BFVcq2DE/hG49/oePinyCep71kfdwnWRX5VLi2UfPaJBTbmcphun8V3BjID2/6q2RO3SpcCAAQZ7eSFo7umDB/rKZA8eULf4s2d0/Tr16Rqpksko2FpZ0e3vv6ez70KFKMvxtm1pP/fd8gQTJ9KStIIF6c+3YEH9xdmZKrba22fnnRNOeGJgHKjf8ccfwA8/0M/UoCAqedmvn9StyrPuvLqD3Xd24787/+HM0zMpKngBgKXcEjKZLMV2uUyOyq6VUadQHdTzrIeeVXpmvQFPngD799OXvosLsGAB1T9u2JDyYzLjtXcvTfSztqYp3uXK0SmqES2lS0ykYB0WRhftWfnr13RW/uiRPsjHxX30cADo7NzSks7Q5XL6wdCuHf35FihAv13OnEn7+U+fUmdhdnGgNjAO1O+IjqayQLNmURGGbt2AdeukbpVZiE+Ox4XnF3DqySmcenIKZ4PO4lXcq3SfU7FgRVwbck13f9bJWXDL54aqblXhVdALVhZW6b/onDnA2LHUd3joEF37+lJqypEjc+JtsdyiVtNkzydP9MH6v/9o5r6JEYKWgN2/T0Fbu6Ts2TO6BAXRzPSsejewly1LQV2bzyerOFAbGAfqVBw9Cnz6KU3VDAricWoJCCFw/819+Af542zQWfg/88flkMtI0qQcGCxboCx8SvjAy9kLI/aPgFqoAQAKmQJlncuikkslVHSpiPqe9dGkeJOUL1K7NuW2XLIE6NqVTkmSk3nIw1T8/DMwaRL1Dz97BjRqlCezCgpBv0MiI/WXN2/ozPzd8fN3x9FDQ2mSXGrTrpydU0/jmhkcqA2MA3Uq4uJoICcpiVIllZFoYhNLIT45HpdDLuP4o+PYd28fTj89rcuO9i4ZZCkmqAFA14pdsb7DegBAsiYZnf9ui1Lr9qPsK6DUiu0o+SgC7h16Q16yFAVqZvyeP6c172o19YSp1TRLS5sK2MwJQRPjXr2iFKqRkXStVgOff569Y3OgNjAO1O85cYLGKZ8+pZ+rS5YAAwdK3SqWioj4CBx+cBhng87i2otruP7i+geVvrTyKfOhTZk2+KzEZ3C2cUa7jR/m8LZOAorDAX1aTcDoeqMBUFC/FHwJReyLwMXWxWwTIxmtL74ADh+mpZUnTtBsqY0bpW5VnseB2sA4UL/n/frU/B/fpLyJe4PbYbdxO+w2Al8F4tqLazjx+ASiE6NT7PftWUAtB45WyodnDgpExUdA8zYGj6gzAnM/mwtLhSUevHmAkvMpIYtKoUIhu0IolL8QCtsVRqH8hdCsZDNd9TC1Ro3oxGjYqew4oBvKkye0POvhQ6p+JpfTIC9Xv8tVeTpQL1y4EHPnzkVISAiqVKmCBQsWoHbt2mnuv2XLFkyaNAmPHj1C6dKl8csvv6BVq1a6x4UQmDJlCpYtW4bw8HDUr18fixcvRunSpTPcJg7U79FoaC2Ddm2FqyvN9OAvXpOVpE7C2aCzOHj/IE48OYGQe1dwa0YE5ACKfAc8dUj9eQWsC6CgbUE8Dn/8QYlPrYkNJmL6p9MBAHdf3UWZP8tApVDBNZ8rXGxdUNCmIAraFkRBm4JoVrIZmpVsBgBIVCfi7qu7cLJ2gpO1E1QWqpx/4+ameXNKCD5iBCWuYbkm11OISmXTpk0YOXIklixZgjp16uD3339H8+bNERgYCBcXlw/2P3PmDLp27YpZs2ahTZs2WL9+Pdq3b4+LFy+iYsWKAIA5c+Zg/vz5WLNmDYoXL45JkyahefPmuHnzJqysPjLjlaVOLqdJKdu301TJ0FCqqlS+vNQtY1lkqbBEg6IN0KBoAwCA2LULUHyJiPIlMLrLMDy+cBiPT/yHx25WeF7UCaHRoVALNV7FvUp15rlcJodbPjc4WDngWdQzbLq+CSWdSuJlDM3QSVAn4EnEEzyJeJLiebaWtrpA/eDNA1RcXFH3mLWFNRytHeFg5QAHKwf0qtILA2rQuuDw+HAsOr8I+ZX5YaeyQ35VfuRX5kd+VX7kU+aDWz43ONuY+ZpvIagb/OBBYPlyyt3u6Ch1qxhM7Iy6Tp06qFWrFv78808AgEajgaenJ4YPH45x48Z9sH/nzp0RExOD3bt367bVrVsXVatWxZIlSyCEgIeHB0aNGoXRo2k8LSIiAq6urli9ejW6dOmSoXbxGXUq5s+nX+VOTnRmvWABMGyY1K1iOSksjCYjVa5MCZv//BMYOhT4809ohAavYl8hJDoED8Mf4v7r+7j/5j7uvLqDi8EX0102ppQr4WLrAidrJ9hY2sBCbgGZTAaN0KCCSwVULFgRSoUSwdHB+M3vN8QkxUAjNB8cZ3LDyZjWZBoA4NbLWyi/KO0fit/V+Q7/a/E/AJQFzmuhF2wsbWCrtIWNpU2KS9sybTGo5iAAQGxSLKYdmwYrC6sUF5WFClYWVijlVAq1C1GPn1qjxvnn56FUKFO9WFtYGzb167uSkwFvb+DCBZqtf+8eL7HLZXnyjDoxMREBAQEYP368bptcLoePjw/8/PxSfY6fnx9GvveH1rx5c+zYsQMA8PDhQ4SEhMDHx0f3uL29PerUqQM/P78MB2qWCm3hWm21+aNHOVDnNc7O+sxjR4/S9dt/d7lMTt3VtgVRybVSiqcJIfA08ikuBl/E1dCruP/mvi6Qh0SHIFGTiKCoIARFBX3wkqefns5Q0xQyBRZfWIz99/ejsF1h2KvsUdOjJgDKoZ6kTkJcchzikuIQkxSD2ORYHH90HDFJMbjz6g6iEqMQlRgFxHx4bCEEPO08obJQIToxGnPOzPlwp7c6le+EP1r+gXzKfEjWJMN7Rdq1EDpX6IyNHWkuh1qjhvUMa1jILWCpsISl3BKWCktYyC1gIbdA85LNsaTNEt1z66+sD43QQCFTwEJuAYX87bVMgeru1fHzpz/r9u2/qz/i1fGQy+RQyBRQyBR0u94blLQERll6UKBetAg/14hBVGI0FHLa592Lez539K/RX3fcFRdX4E38G8hlcsggo2sZXTtaOaJ75e66fbfd2obXca8hgwwymSzFta3SFh3Ld9Tte+j+IYTFhumOB0C3v1KhxOdl9dOvzzw9g5cxL1PsBwAymQwKmQItS+tTX18MvogXMS9S7PPuc5qWaAq5jJK/XH9xHaHRoQAAKwsr1C9SP81/x9xgMoE6LCwMarUarq6uKba7urri9u3bqT4nJCQk1f1DQkJ0j2u3pbVPahISElLkNI+MjMz4GzEXFSrQmtpXb8+cjh2jsWsjynrEski7lEcrNFSfdLlx448+XSaToYh9ERSxL4L25dqneCwhOQHB0cF4HvUczyKfISw2DG/i3+B13Gu8iXuDBHUCkjRJSFQnIj45HpEJkYiIj0B4fDgiEiJ0E97UQo2XsS/xMvYlzj0799E2/RXwF/4KyFjd7H339mHfvYxlu9h8czM239ysf+9vA5g2AAgIqDVqCAgEhgVi7um5sJBbQAiBJE0SkjRJqY7tP4l4gsiESMQkxiAyIRJng86m2qsAAM+jnqNh0YbQCA1iEmOw7tq61OcLOAH1vYDvN11AnFN+2N6/j4Wn/4eQpNTLZ1V1q5oiUM8+PRv3Xt9Ldd/STqVTBOppx6fhaujVVPf1yO+RIlBPPjYZZ4POprqvg5UD3ozVt2/y0cnwfeib6r6WckskTtJn6Jt2fBp2Be5KdV8ASJqUpPt3mnlyJjZc3wAAKGxXGE+/z0TVkRxgMoHamMyaNQvTpk2TuhnGTS6n2d7h4ZRk9/VrytBfpYrULWPZ1bkzBedffgHq1dMnyKhShX6cZYPKQoViDsVQzKFYlp6vERpEJ0YjIj4CoTGhCIoMwtOIpwiKDEJITAhCo0PxIuYFwmLDUgQ2uUwOW6UtbC1tkU+ZD0qFMsWsc43QQCM0UGvUSNYk634oxCfHI0GdoHtMIzRI1iRDLdS6feOT43Vr0gUEPfY2qcy7LodexuXQyxl6n/vu7YP97IwlnL4SegXN1zb/+I4CaHkHkMfEYlQT4GxhwPONBlWrtEABmwKwsbSBUqGERmgghEAhu0JISE5AeHw4wuPDUbtQbRS1L0qfh0aje59J6iSoFCoM2zsMr+NeIzYpFhHxEXC2cYZGo9Gd/Wsv9lb2WBawDI7WjlApVLBT2aFcgXJI1iTrLkmaJCRrkqFUKNF3Z1/YWNrA2tIa4fHh8MjvQW9HCN2/m/bfuuf2ntRLIbfE04incLV1hUKmgEwmg1rQv5daQ+3+fMPnute69+oebCypYEl0YjTabWyHnV12ZujzzwkmE6idnZ2hUCgQGhqaYntoaCjc3NxSfY6bm1u6+2uvQ0ND4e7unmKfqtqq56kYP358ii71yMhIeGozujO9hQvpulUryrd35AgHalMXE0M5ouPi9JWWjhyh608/la5db8llctip7GCnsoOnvaeuu1tKQgjEJcchOjEakQmRCI4KxtPIp3ga8RQh0SG6oJOkpjNobcBIUlPPQWxSLGKTYhGTFIOohCjqRUiIgEZoUrxfO5UdbC1tdT845DI5YpJiEJMYg5ikGChkCt24u7WFNQQEEtWJuh8d4fHhUDsGAYjEgAvA0hoAZBHA/f2696JUKOGWzw3JmmS8iXuDSUcnZfhzOPf84z0bWgN2Z7w4yMrLKzO87z9XM152N61ek9ikWJx8fDLDx8kJJhOolUolatSoAV9fX7Rv3x4ATSbz9fXFsDTGPr29veHr64vvvvtOt+3QoUO6mtnFixeHm5sbfH19dYE5MjIS/v7+GDx4cJptUalUUKl4KUiGNWlCgfroUSppw0zXgQMUpIsX1//oem98mqUkk8l0E9FcbF1Qyin7qVWFEEhQJ0ClUOXsevNOYUDhwqgekgDfM6WwR3YPZ+p44H7+JLyMfYlEdeIHM/EBwF5lr5txn1+ZHyoLFSzlllAqlLCxtNEtoXO0coSt0hYqhQpKhRIWcgvEJMXozsrD48PxJv4N3sS9wZv4N0hITkA+ZT7djw87lZ1uVr+dyg5qjVr3QyYuOU53tg/oP3drC2tYW1pDIVPozpC1cxSiE6MRk0hzFPIr88PRyhFO1k6wt7KHUqHUvQeFXAEhBAQEhBCwVFjm3GeeASYTqAFg5MiR6NWrF2rWrInatWvj999/R0xMDPr06QMA6NmzJwoVKoRZs2YBAEaMGIFGjRrh119/RevWrbFx40ZcuHABf/1FY1EymQzfffcdfv75Z5QuXVq3PMvDw0P3Y4Blk0ZDeYQBynr0/vgmMy3aWoFffEHr4oOCKF2oXE4Vs5hByGSyjxdMyQpnZ+Crr4C1a/GpdXl8uuseEJAABAUh0VKOkOgQBEcFQ6lQwsHKAY7WjsivzA+FnP9P5yphYhYsWCCKFCkilEqlqF27tjh79qzusUaNGolevXql2H/z5s2iTJkyQqlUigoVKog9e/akeFyj0YhJkyYJV1dXoVKpRNOmTUVgYGCm2hQRESEAiIiIiCy/rzyrTx8hACFUKro+f17qFrGsSkgQwt6e/h1PnaJtf/9N92vVkrRpLAdduybEnj30712kCP37rl4tdavynMzEDZNaR22seB11OpYuBQYN0s8AnzMHGDNG6laxrDh4kDJXubrS+mm5HOjTB1i9mkpdzp4tdQtZTps1C/jxRyrScS7jY8zs4zITN3itDMtd2nHL8HC61k48YqZn+3a6bt+egnRyMrD/7UQjI5hIxnJBz56Us//8eeDiRalbY7Y4ULPcVbo01aRWv12OcvIkFepgpsfHh2r7ffUV3T98mAr2OjtnaP00MzGzZlFFrfpvk3v8lbF15izncaBmuUsm059VW1vT8p7z56VtE8uaDh2AnTuBpk3p/j9vl7p06QIoldK1i+WOZ8+AFy/0P7LXrQOio9N/DssVHKhZ7tN2i76/7paZrqgofVd4z57StoXlDu0S1TNnaDledDSXq5UIB2qW+7Rn1G/epvrzTT3FHzNSQgBLlgAPHui3bd1K66nLlgVqSp9UhOWCChVoyZ1aTYU6AO7+lggHapb7ihcHZs4EVqyg+2fOALGx0raJZdy1a3R2VaGC/t/t77/pumdPrjOel2nPqq9e1U8qu3RJ2jaZIQ7UzDDGjwd69QI8PYHERODUKalbxDJK28XdrBkNXzx5os/v3b17mk9jecCXX9JyvNBQWqIF8Fm1BDhQM8ORyfQTkbj723RoA/UXX9D1unXUHd6oEVC0qHTtYrlPqQQGDqTb2pK1PKnM4DhQM8MQAti9m36ZAxyoTcWDB8CVK5T2tW1b+nfUzvbmSWTmYeBA6gJfv57GqqOieFKZgXGgZoYhkwEjR1JxDoCSJ7x+LW2b2Mdpz6YbNqTscgEBwK1bgJUV0LFj+s9leYOHB7BoEVCxIjDgbVWrJUukbZOZ4UDNDEe7TMvJic7MtFWXmPF6v9t75duSgu3bA5wu1/z07k3d4QEBnA/BgDhQM8PRjk9rZwlz97dxi4wELl+m2+3bU/KLVavo/oCM1wtmecTly1SmtnJlur94saTNMSccqJnhaNdTv3pF1xyojZudHc0pOHiQZuvPnw/Ex9PsX04Zan4uXqSJZM+e0f0NG3j4ykA4UDPDcXYGqlal2zIZcOcO1TNmxsvWFvjsM5pAtHAhbRs3jtdOm6OuXWnYKjiYciPEx1PlNJbrOFAzw9KOUzs70zWfVRun96vf/vUXVUArW5a6wZn5sbYG+vWj2yoVXS9eDGg00rXJTHCgZoalHafWFnE4fFi6trC0bdwIVKlC9cQTEoDffqPtY8ZQiUtmnoYMoaV6t29Tb8u9e/x/2AD4fxwzrCZNaHmPtsvM1/fDszcmve3bKW3k48c0Lvn8OS3T+fprqVvGpFS0KGUr094GaOkWy1UcqJlhWVsD5coBn3xCa3GDg+nXOTMe8fH69e6ffw7MmUO3v/9e3+XJzNd339H1vXt0/d9/lFaW5RoO1EwaVlYUrAHuOjM2hw9TishChYCQECAwEHBw4CVZjHh7U23y6dOBBg1ojJrzf+cqDtTM8EJDga++oq5VgAO1sdEmOWnfHli7lm5/8w0nOGFEJgP+/Rf44Qfg229p2/LlVGyH5QoO1MzwHB2BvXspgQZAlZiSkyVtEnsrORnYtYtuN2tG+dkBHptmqWvXDnBzox/fO3ZI3Zo8iwM1MzylkrrMABqzjowELlyQtk2MnD4NhIXRj6nQUJrx7eWlX//OmFZSErB1K1CwIN3nTGW5hgM1k4Z2mZa9PV1z97dxsLens+cePYBNm2hb9+6c4IR9KDoa6NsXuHaN/j6OHaMVHSzHcaBm0tAG6vBwuubEJ8ahalUqYzl2LHDkCG3r1k3SJjEj5ehIRToA/Vk1V9XKFRyomTSqVqWyifHxdP/MGSAmRtImsXds3Ejr2+vVo3SRjKVm5EhKgKOdb7JmDRAbK22b8iAO1Ewacjng40O37exoxuipU9K2ydz5+lKFJCEoyQlA3d6MpaVkSX1d8nz5gIgI+pHHchQHaiadFi2A2rWBSpXoPo9TS2vYMKBaNWDBAqqUZGEBdOokdauYsRs7lq61Z9I8qSzHcaBm0undG/D3p/zBAI9TS+nWLcoQp1Tqyxg2b64vnsJYWqpXp94xjYZ6yi5cAM6fl7pVeQoHaiY97cSyS5doaRAzPG2Sk6ZNKZkFwN3eLOPGjqWMZY0a0f0FC6RtTx7DgZpJz9oaKFWKbmtnGjPD2raNrqtVAx48oMpIn38ubZuY6WjalNbg//IL3d+4kdLPshzBgZpJ6+RJKkb/8iXd5+5vw3v8GAgIoG7LqCja1ro1BWvGMkImo0utWkDdupQMhfN/5xiTCdSvX79G9+7dYWdnBwcHB/Tt2xfR0dHp7j98+HCULVsW1tbWKFKkCL799ltERESk2E8mk31w2cizFg2nalX6D679dzl4kMteGpo29eMnnwD799NtbSlDxjIjPBwoXJhuL17M+b9ziMkE6u7du+PGjRs4dOgQdu/ejRMnTmBAOtV8nj9/jufPn2PevHm4fv06Vq9ejf3796Nv374f7Ltq1SoEBwfrLu3bt8/Fd8JSyJ+fxrYAOqN79EhfPo8ZxsGDdO3tDdy9SxPKWrWStk3MNP37L13kcur63rJF6hblDcIE3Lx5UwAQ58+f123bt2+fkMlk4tmzZxk+zubNm4VSqRRJSUm6bQDE9u3bs9W+iIgIAUBERERk6zhm66efhACEcHam6wULpG6ReUlMFOLgQSFGj6bPv00bqVvETFVCghBFitDfESBE7dpSt8hoZSZumMQZtZ+fHxwcHFCzZk3dNh8fH8jlcvj7+2f4OBEREbCzs4OFhUWK7UOHDoWzszNq166NlStXQnDXq2E1a0bX2qGMAweka4s5srQEPvsMOHSI7nfoIG17mOlSKoHx4/X3z52jJZgsW0wiUIeEhMDFxSXFNgsLCzg5OSEkgzMLw8LCMH369A+6y3/66Sds3rwZhw4dQocOHTBkyBAs+MjSgoSEBERGRqa4sGyoWRNwcNCnEz16lMe2DO3+feDKFUChANq2lbo1zJT16aMfpwaA+fOla0seIWmgHjduXKqTud693L59O9uvExkZidatW6N8+fKYOnVqiscmTZqE+vXro1q1ahg7dix++OEHzJ07N93jzZo1C/b29rqLp6dnttto1hQK/VpqW1vK+X36tLRtMgevXgGlSwOjRunXTjduTDnYGcsqlQoYN05/f9MmmnvCskzSQD1q1CjcunUr3UuJEiXg5uaGF9qk728lJyfj9evXcHNzS/c1oqKi0KJFC+TPnx/bt2+HpaVluvvXqVMHQUFBSEhISHOf8ePHIyIiQnd5+vRpxt80S92AAcCiRdQFC3D3tyHs3EkT9w4f1s/85tneLCf07Qu4u9NttRqYPFna9pg4i4/vknsKFiyIgtryaOnw9vZGeHg4AgICUKNGDQDAkSNHoNFoUKdOnTSfFxkZiebNm0OlUmHXrl2wsrL66GtdvnwZjo6OUKlUae6jUqnSfZxlgXacOn9+ChoHDwKzZ0vapDxv61a6btYMmDePbvOKB5YTrKyACROAffuAPXuAtWup56ZKFalbZpJMYozay8sLLVq0QP/+/XHu3DmcPn0aw4YNQ5cuXeDh4QEAePbsGcqVK4dz584BoCDdrFkzxMTEYMWKFYiMjERISAhCQkKgVqsBAP/99x+WL1+O69ev4969e1i8eDFmzpyJ4cOHS/ZezZ42YF+6BISGStuWvCwiQj95TPujs1494O3/J8aybehQYPduoHNnmgP+7iQzlikmEagBYN26dShXrhyaNm2KVq1a4ZNPPsFf72S+SUpKQmBgIGLfVnC5ePEi/P39ce3aNZQqVQru7u66i7ar2tLSEgsXLoS3tzeqVq2KpUuX4rfffsOUKVMkeY9mLzSUzvK0E1G0gYTlvN27KXtUuXJUCxzgbm+WO37+mSqx7dsHHDsmdWtMkkzwWqRsi4yMhL29vW75F8uigwepYlO+fLRU6+uvgX/+kbpVedOXX1Ihju+/B/74gyof3b8PlCghdctYXnP/PpW0vXePytqePUvZCM1cZuKGyZxRMzPQsCFgY6NfT33wIAUQlrOio+nsBqBub42GcjRzkGa54cIFfbbBc+f0BWBYhnGgZsbDygpo0oRuW1oCL17Q2l6Ws2JjgW++AerXB06dom2dO0vbJpZ3ffUV1azWGjeO8yRkEgdqZlxatqTr/Pnpmpdp5TwXF2DhQipFqF2v3qmTtG1ieZdcDsyapb9/7x7Xq84kDtTMuGgDdXg4XWu7aFnO27KFZuPWrw9w0h6Wmz77DPj0U/39n36iHjOWIRyomXEpUQIoU0Y/Nn36tD5os+y7dAk4cYKSUGzaRNu6dJG2TSzvk8lS5kWIjAQmTpSuPSaGAzUzPi1bUlrRggUpoGjLMLLs++UXoFEjmu3t70/dkh07St0qZg5q1QJ69tTfX76cfjiyj+JAzYzP+PGUh7pXL7q/Z4+07ckrYmOB//6j29pVmY0bAx9Jw8tYjpk9G5gyhSaYCQGMGKH/W2Rp4kDNjI+rK2BvD7RqRff37eNlWjlh714K1sWLAydP0jae7c0Myd0dmDoV+PVXwNqa/g43b5a6VUaPAzUzXp98QslPXr6ktZgse7RfiD4+tOzNwoKzkTFpeHoCY8bQ7TFjqGIeSxMHamacAgKABg3oVzdAZ4Ms62JiKG0oQOPSAAVsZ2fp2sTMV2Skfhjm6VMuwPMRHKiZcXJ2pslOYWF0n8eps2fvXiAuDihZkmZ9A9ztzaRjZwcUKqS/P2cO8OCBdO0xchyomXEqWhSoXFk/0eTCBa6mlR3aAieNGwO3bgFKJfDFF5I2iZm5P/7Q95glJgIjR0rbHiPGgZoZr7Zt6drRka45+UnWLVkC+PnpvxhbtKAJe4xJpUQJYOZM/f2dOzkTYRo4UDPj9fnndK2daMLd31knlwN16ui/CLnbmxmD4cMBb++U9zkP+Ac4UDPjVbMmrfHV/sc9eJBqKLPM0S5tu3wZuHuXip9oeysYk5JCAaxcSVXcAPr7fDcvOAPAgZoZM7kcaN2abltb00xRbREJljHh4UDhwsDAgcC6dbStdWt90RPGpFauHK2t1pZZnT6dalYzHQ7UzLh17Ah06ADUrUv3d+6Utj2mZts2IDgYOHMG2LqVtnG3NzM2o0cDgYFA166UNvjrr/V16RkHambkWrQA/v0X+PZbur9jB6cczIz16+m6QQPg0SPA1lbfS8GYsbCwoMuiRZQM5f59ykfPAHCgZqaiWTPq/n70iLJqsY8LDgaOHqXb2nH+tm0BGxvp2sRYeuzt9eUwly8Htm+Xtj1GggM1Mw1PnwJeXnR7xw5Jm2IyNm+miWR16/Jsb2YaZDI6s9bq2xd4/Fi69hgJDtTM+IWE0IQTbUk8/pWdMRs20HXdukBQEGWDatFC2jYx9jHz5wMVKtDtN2+oOE9EhLRtkhgHamb83NyAGjVobFomA65e5XSDH/Pggb7etPZLrl07WprFmDGzsaF5Kdohmps3qSymGS/N5EDNTIM23aWTE11z93f6rKyornfPnvpiHN26SdsmxjKqXDlaTqgtIHPoECVDMdOJpByomWno0IGuw8Ppmru/0+fhQekZO3WiMqEFC1K1LMZMRfv2wIIF+vtLl1IdazPEgZqZhnLlaNxKrab7p08DL15I2yZToE1y0rlzykk6jJmCIUOACROo6xug2tXz50vbJglwoGamQ3tWbW9PXWC7dknbHmO1di3V+g0P1w8RdO8uZYsYy7qffwY2bQJ++IHujxgBzJsnbZsMjAM1Mx3aQK2dVMLj1B9KTqazjs8/py+4mBhKzVinjtQtYyzrZDJg9mxg1Ci6P2ZMyspbhrJnD6U4NTAO1Mx0VKoE7N0LnDhB9w8dAqKipG2TsTl0iJazOTvTbFmAJpHJZNK2i7Hsksno71prwgS6GGKCmRD0Q6FtW2DyZGD//tx/zXdwoGamQyYDWrYEqlcHypShbFv//Sd1q4zL33/T9RdfUNAGuNub5R1jx9LZtNbMmZQfPCEh915TCMo9Pn483e7cmVLxGhAHamZ6ZDKazQzQ2BUjERH64YACBagbvHp1mojHWF4gkwFz5gBz5+q3bdpENa1fv86916xShSZjtmhBhW769AHi4nLn9VLBgZqZngUL9MUm9u/XL9kyd1u2APHxQPny+uEBXjvN8qLRoyktboECdP/SJRoau3YtZ44fFpaypkCFCoCrK33fJCXRj18DDrtxoGam59Ejyrxlb0/d31z6kmi7vdu2pbKWMhnQpYu0bWIstzRrBty5Q9cKBfD8OVCtGs0Kf/Mma8d8+RL45RegbFkqsXv1Kg0jtWkDPHtGtd23baMhNxeXnH0/6TCZQP369Wt0794ddnZ2cHBwQN++fRH9kXqljRs3hkwmS3EZNGhQin2ePHmC1q1bw8bGBi4uLhgzZgySk5Nz862w7NLO/o6Pp2vu/gZiY+kXvlyur5T16adAoULStoux3OTkRGe5Fy/S94JaTeusXV1p5cPJkx9PPZqYCBw7Rr1PhQoB48ZRN/qbNxT4d+ygHwKjRgG3blHgNvDkTJkQppGTrWXLlggODsbSpUuRlJSEPn36oFatWliv7QJNRePGjVGmTBn89NNPum02Njaws7MDAKjValStWhVubm6YO3cugoOD0bNnT/Tv3x8zMzH1PzIyEvb29oiIiNAdm+UijQYoUoR+4QI0dhQSou8GM2d371IGsidPaHiga1epW8SY4Rw6BHzzDRWh0bK0pOWJdeoAKhX1ONWtS4+tW0fjze8GcxcXGk57tzTs7Nk0pJSDMhU3hAm4efOmACDOnz+v27Zv3z4hk8nEs2fP0nxeo0aNxIgRI9J8fO/evUIul4uQkBDdtsWLFws7OzuRkJCQ4fZFREQIACIiIiLDz2HZNGqUEIAQ9vZ0vWyZ1C0yDgcO0Ofh6ChEXJzUrWHM8GJihOjeXQgrK/q/8P6leXMhVq4UYvVqIYYPp21KpRAqVcr9atcW4vjxXGtmZuKGSXR9+/n5wcHBATVr1tRt8/HxgVwuh7+/f7rPXbduHZydnVGxYkWMHz8esbGxKY5bqVIluLq66rY1b94ckZGRuHHjRprHTEhIQGRkZIoLMzDtkqOYGLrevFm6tkgtKAjQ/g2uWEHX3btzpSxmnmxsKDtfVBR1WzdsmLKr+sABOuvu3VufSzwxkZZ4OTsD/fsDBw8CZ8/Sc42ASST/DQkJgct7A/cWFhZwcnJCSEhIms/r1q0bihYtCg8PD1y9ehVjx45FYGAgtm3bpjvuu0EagO5+esedNWsWpk2bltW3w3JC1aqAlxeNGQHAkSP64hPmZswYSqc6b55+eVbfvpI2iTHJWVhQadd27YDgYCAggIaG7t4F7t2j+RyOjjTOXaAA0LgxBWYjzIkvaYvGjRuHX375Jd19bmm/iLNgwIAButuVKlWCu7s7mjZtivv376NkyZJZPu748eMxcuRI3f3IyEh4enpm+XgsC2QyYOBAWpbh7w/cvg1s3Qq8N1kwzwsLo1moiYnA/ft0Xb06/ZBhjBF3d5q5baIkDdSjRo1C7969092nRIkScHNzw4v3KiUlJyfj9evXcHNzy/Dr1Xmb7/jevXsoWbIk3NzccO7cuRT7hIaGAkC6x1WpVFCpVBl+XZZLRoyg67lzKWH/pk3mF6j//lsfnA8epG18Ns1YniJpoC5YsCAKZqCr0tvbG+Hh4QgICECNGjUAAEeOHIFGo9EF34y4fPkyAMDd3V133BkzZuDFixe6rvVDhw7Bzs4O5XN4hh/LRZ06UaA+fpxmOxcpInWLDEMIYNkyuv3ZZ7T+08qKk5wwlseYxGQyLy8vtGjRAv3798e5c+dw+vRpDBs2DF26dIGHhwcA4NmzZyhXrpzuDPn+/fuYPn06AgIC8OjRI+zatQs9e/ZEw4YNUblyZQBAs2bNUL58efTo0QNXrlzBgQMHMHHiRAwdOpTPmE2FEMCrV1QhSgjgn3+kbpHhnD5NXf42Nvra3B06AA4OkjaLMZbDcm3ueQ579eqV6Nq1q8iXL5+ws7MTffr0EVFRUbrHHz58KACIo0ePCiGEePLkiWjYsKFwcnISKpVKlCpVSowZM+aDqfCPHj0SLVu2FNbW1sLZ2VmMGjVKJCUlZaptvDxLQmFhQlhY6JdUlC4thEYjdasMo0cPes89ewphZ0e3jxyRulWMsQzITNwwmYQnxowTnkisTRuqE2tpSYkLzpyhJP15WWwsJWaIiaFMSrNnA6VKAYGBNJuVMWbUMhM3+H80M33aNdXa4YrVqyVrisHY2FC39x9/APv20bbBgzlIM5YH8f9qZvrataMCHdrc75s2GbQEnWQKFwZq16YqP1ZWlMCBMZbncKBmps/GRn9WbWNDdZl37ZK2Tbnp3bzEixfTdefOlLiBMZbncKBmeYM2uY22olZe7v7u1IkK2J84oa8cNniwtG1ijOUa48uVxlhWVKkC1KpFtapfvqTkH8+fA2+X7+UZDx5Q/W0h6D0nJFApvtq1pW4ZYyyX8Bk1yzv+/ZdKX9avT6Uw166VukU5b+FCCtLNmlHKVAAYMsTg9XEZY4bDgZrlHUWK0BIt7aSqZcsoYOcV0dH66lgNG1Jub3t7rjnNWB7HgZrlPZ06AfnyUYWc/fulbk3O+ftvmihXpgwVIgGAnj0BW1tp28UYy1UcqFneEhoKVKyoX541f7607ckpGo3+vXz1FfDff9TdPWSItO1ijOU6DtQsb3FxoWVKajXdP3CAsnWZusOH6X3Y2QEPH9K29u2BcuUkbRZjLPdxoGZ5i0ymX6ql7RL+80/p2pNTGjcGli8Hhg0DNm+mbePGSdokxphhcKBmeU/PnlRBKiaG7q9eDURGStmi7FMqqc50dDSQnAx8+ikvyWLMTHCgZnlPvnzAoEF029qagpspJ0DRzlwPC9PXn+azacbMBgdqljcNH05LtbSTyhYsMM2lWleu0CzvFStoMllcHFCjBuDjI3XLGGMGwoGa5U0eHkC3bnRbpTLdpVozZ9J66X379GPt48ZxghPGzAgHapZ3TZxItamHDaP7M2dSVi9TERgIbNlCt4sWBd68obPrL76Qtl2MMYPiQM3yrlKlAG9vYORIOqs+fRo4dkzqVmXc7Nn0w6JVK0p2AgBjxwIKhbTtYowZFAdqlvd5eABff023p0+Xti0Z9egR8M8/dLtoUZpIVqoUzWhnjJkVDtQs75s3D1i/HpDLgaNHgVOnpG7Rx02aRElbmjQBNm6kbVOmABZc8I4xc8OBmuV9hQrRbGltkDP2s+oHD4B16+h2mTI0Nl2uHBffYMxMcaBmeV/nzlS7OTGRZksfPKgvamGMSpSgs/6JE/Vn01On8tg0Y2aKAzXL++RyYMYM/W3A+M+q69WjiWQREUClSlSIgzFmljhQM/PQqhVQv76+WMeePYCfn7Rtel9yMhAURLfDwoA//qDb06bpf2AwxswO/+9n5kEmA2bN0t8GaH21NnAbg1WrgNKlgV9+oaQm0dFA9epUJYsxZrY4UDPz0aAB0LIlFbiwtgYuXgT++kvqVpHISGDyZCA+ns6qV6yg7QsWcBYyxswcB2pmXhYsAO7eBebMofsTJgAvX0rbJgD44QcgJAQoWZJqTwNA//40Vs0YM2scqJl5KVkS8PSk6lpVqtDSp/HjpW3TsWPA0qV0u3Fj4PZtoGBBykzGGDN7HKiZebKwAAYMoNsrVki3XCs2FujXj2537apfP/3bb4CTkzRtYowZFQ7UzDydOwcMHaqfTd2vH3U9G9qkSVQdq3Bh4MULGqNu2hTo3t3wbWGMGSUO1Mw81apFE8s0Gjq7vn6d6jwbesmWmxsVDGnbFvD1pduLFvEEMsaYDgdqZp5kMmDxYsDWltYvu7kBz58DjRrRdkOVwxwzBti1Sz/Le/ZsShvKGGNvmUygfv36Nbp37w47Ozs4ODigb9++iI6OTnP/R48eQSaTpXrZoq3xC6T6+EZt2kaWtxUtSjWqAVqz/NlnQFISMGQI0KmTPvlITlOraRIbAERFURd8YiLw+efAiBG585qMMZNlMoG6e/fuuHHjBg4dOoTdu3fjxIkTGKCdDJQKT09PBAcHp7hMmzYN+fLlQ8uWLVPsu2rVqhT7tecEE+Zj6FCqUBUdTcu2pkyhnNr//guULUtnuAkJOfuaEyZQN/vVq8DAgcC9ezQTfdUq7vJmjH1ImICbN28KAOL8+fO6bfv27RMymUw8e/Ysw8epWrWq+Oabb1JsAyC2b9+erfZFREQIACIiIiJbx2ESCQsTomRJIQAhfv9diMuXhfjkE7oPCFG6tBAbNwqRnJz911q/Xn/cfv3oWqEQ4vTp7B+bMWYyMhM3TOKM2s/PDw4ODqhZs6Zum4+PD+RyOfwzuKwmICAAly9fRt++fT94bOjQoXB2dkbt2rWxcuVKiI+MTyYkJCAyMjLFhZmwAgWA//6jsekRI2h99YkTwD//0Nj13btAly6AlxewciV1U2fF+fPAN9/Q7WbN6FgAFQzhxCaMsTSYRKAOCQmBi4tLim0WFhZwcnJCSAaX1KxYsQJeXl6o994X4k8//YTNmzfj0KFD6NChA4YMGYIFCxake6xZs2bB3t5ed/H09MzcG2LGx8uLkqBoxcYCX38NBAZSUQwnJwrYfftS0pRffgFev8748Y8dA3x8aPlVqVJUalOjoexjY8bk+NthjOUhuX+Cn7axY8cKAOlebt26JWbMmCHKlCnzwfMLFiwoFi1a9NHXiY2NFfb29mLevHkf3XfSpEmicOHC6e4THx8vIiIidJenT59y13deEh0tRI0aQgwbJkRSEm2LihJi3jwh3Nz0XdfW1kIMHCjEtWvpH2/LFiGUSnqOq6v++dOmCaHR5P77YYwZncx0fcuEMNQ6lA+9fPkSr169SnefEiVKYO3atRg1ahTeaGfKAkhOToaVlRW2bNmCL774It1j/PPPP+jbty+ePXuGggULprvvnj170KZNG8THx0OlUmXofURGRsLe3h4RERGws7PL0HOYEdu5U1+xqmVLYONGQPvvGh8PbNhAJSivXNE/p3Jl6h7v3BkoUUK/XaOhbm1/fyoEEhdHk9WWLqWzc8aYWcpM3JA0UGfUrVu3UL58eVy4cAE1atQAABw8eBAtWrRAUFAQPDw80n1+48aN4ezsjH///fejrzVjxgz8+uuveJ2Jbk0O1HnQtm3U9R0XB1SsCKxfD1SqpH9cCBrH/uMPYPduWtalVagQ4O5O10olrZPWzhx3dgbWrKH62Iwxs5WpuJHLZ/c5pkWLFqJatWrC399fnDp1SpQuXVp07dpV93hQUJAoW7as8Pf3T/G8u3fvCplMJvbt2/fBMXft2iWWLVsmrl27Ju7evSsWLVokbGxsxOTJkzPVNp71nUedP5+yq/vLL4W4dOnD/V69EmL5ciF8fISQyfT7v3upVk2IVauEiIsz9LtgjBmhzMQNC0P8csgJ69atw7Bhw9C0aVPI5XJ06NAB8+fP1z2elJSEwMBAxMbGpnjeypUrUbhwYTRr1uyDY1paWmLhwoX4/vvvIYRAqVKl8Ntvv6F///65/n6YCahZk3KCjxpF66q3baNJZcuW0ePBwcCtWzTx7OFD6ua2sQFiYujxceOAfPmoIla9erxGmjGWJSbR9W3suOvbDNy4QVnMpk/Xj0GvXg306fPhvuXKAb/+SuPbHJwZY6nITNwwmTNqxiRVoYK+BKWWQkF5uS0sgDp1gLp1AW9v2lduEisfGWMmgM+ocwCfUTPGGMuMzMQN/tnPGGOMGTEO1IwxxpgR40DNGGOMGTEO1IwxxpgR40DNGGOMGTEO1IwxxpgR40DNGGOMGTEO1IwxxpgR40DNGGOMGTEO1IwxxpgR40DNGGOMGTEO1IwxxpgR4+pZOUBb1yQyMlLiljDGGDMF2niRkbpYHKhzQFRUFADA09NT4pYwxhgzJVFRUbC3t093Hy5zmQM0Gg2eP3+O/PnzQyaT6bZHRkbC09MTT58+5fKXGcCfV8bxZ5U5/HllHH9WmZPVz0sIgaioKHh4eED+kfr1fEadA+RyOQoXLpzm43Z2dvwHnwn8eWUcf1aZw59XxvFnlTlZ+bw+diatxZPJGGOMMSPGgZoxxhgzYhyoc5FKpcKUKVOgUqmkbopJ4M8r4/izyhz+vDKOP6vMMcTnxZPJGGOMMSPGZ9SMMcaYEeNAzRhjjBkxDtSMMcaYEeNAnU0LFy5EsWLFYGVlhTp16uDcuXPp7r9lyxaUK1cOVlZWqFSpEvbu3WuglhqHzHxey5YtQ4MGDeDo6AhHR0f4+Ph89PPNSzL7t6W1ceNGyGQytG/fPncbaGQy+3mFh4dj6NChcHd3h0qlQpkyZczm/2NmP6vff/8dZcuWhbW1NTw9PfH9998jPj7eQK2VzokTJ9C2bVt4eHhAJpNhx44dH33OsWPHUL16dahUKpQqVQqrV6/OfkMEy7KNGzcKpVIpVq5cKW7cuCH69+8vHBwcRGhoaKr7nz59WigUCjFnzhxx8+ZNMXHiRGFpaSmuXbtm4JZLI7OfV7du3cTChQvFpUuXxK1bt0Tv3r2Fvb29CAoKMnDLDS+zn5XWw4cPRaFChUSDBg1Eu3btDNNYI5DZzyshIUHUrFlTtGrVSpw6dUo8fPhQHDt2TFy+fNnALTe8zH5W69atEyqVSqxbt048fPhQHDhwQLi7u4vvv//ewC03vL1794oJEyaIbdu2CQBi+/bt6e7/4MEDYWNjI0aOHClu3rwpFixYIBQKhdi/f3+22sGBOhtq164thg4dqruvVquFh4eHmDVrVqr7d+rUSbRu3TrFtjp16oiBAwfmajuNRWY/r/clJyeL/PnzizVr1uRWE41GVj6r5ORkUa9ePbF8+XLRq1cvswrUmf28Fi9eLEqUKCESExMN1USjkdnPaujQoeLTTz9NsW3kyJGifv36udpOY5ORQP3DDz+IChUqpNjWuXNn0bx582y9Nnd9Z1FiYiICAgLg4+Oj2yaXy+Hj4wM/P79Un+Pn55difwBo3rx5mvvnJVn5vN4XGxuLpKQkODk55VYzjUJWP6uffvoJLi4u6Nu3ryGaaTSy8nnt2rUL3t7eGDp0KFxdXVGxYkXMnDkTarXaUM2WRFY+q3r16iEgIEDXPf7gwQPs3bsXrVq1MkibTUlufcdzru8sCgsLg1qthqura4rtrq6uuH37dqrPCQkJSXX/kJCQXGunscjK5/W+sWPHwsPD44P/CHlNVj6rU6dOYcWKFbh8+bIBWmhcsvJ5PXjwAEeOHEH37t2xd+9e3Lt3D0OGDEFSUhKmTJliiGZLIiufVbdu3RAWFoZPPvkEQggkJydj0KBB+PHHHw3RZJOS1nd8ZGQk4uLiYG1tnaXj8hk1MwmzZ8/Gxo0bsX37dlhZWUndHKMSFRWFHj16YNmyZXB2dpa6OSZBo9HAxcUFf/31F2rUqIHOnTtjwoQJWLJkidRNMzrHjh3DzJkzsWjRIly8eBHbtm3Dnj17MH36dKmbZjb4jDqLnJ2doVAoEBoammJ7aGgo3NzcUn2Om5tbpvbPS7LyeWnNmzcPs2fPxuHDh1G5cuXcbKZRyOxndf/+fTx69Aht27bVbdNoNAAACwsLBAYGomTJkrnbaAll5W/L3d0dlpaWUCgUum1eXl4ICQlBYmIilEplrrZZKln5rCZNmoQePXqgX79+AIBKlSohJiYGAwYMwIQJEz5aotGcpPUdb2dnl+WzaYDPqLNMqVSiRo0a8PX11W3TaDTw9fWFt7d3qs/x9vZOsT8AHDp0KM3985KsfF4AMGfOHEyfPh379+9HzZo1DdFUyWX2sypXrhyuXbuGy5cv6y6ff/45mjRpgsuXL8PT09OQzTe4rPxt1a9fH/fu3dP9oAGAO3fuwN3dPc8GaSBrn1VsbOwHwVj7A0dwBuoUcu07PltT0czcxo0bhUqlEqtXrxY3b94UAwYMEA4ODiIkJEQIIUSPHj3EuHHjdPufPn1aWFhYiHnz5olbt26JKVOmmN3yrMx8XrNnzxZKpVL8+++/Ijg4WHeJioqS6i0YTGY/q/eZ26zvzH5eT548Efnz5xfDhg0TgYGBYvfu3cLFxUX8/PPPUr0Fg8nsZzVlyhSRP39+sWHDBvHgwQNx8OBBUbJkSdGpUyep3oLBREVFiUuXLolLly4JAOK3334Tly5dEo8fPxZCCDFu3DjRo0cP3f7a5VljxowRt27dEgsXLuTlWcZgwYIFokiRIkKpVIratWuLs2fP6h5r1KiR6NWrV4r9N2/eLMqUKSOUSqWoUKGC2LNnj4FbLK3MfF5FixYVAD64TJkyxfANl0Bm/7beZW6BWojMf15nzpwRderUESqVSpQoUULMmDFDJCcnG7jV0sjMZ5WUlCSmTp0qSpYsKaysrISnp6cYMmSIePPmjeEbbmBHjx5N9TtI+/n06tVLNGrU6IPnVK1aVSiVSlGiRAmxatWqbLeDq2cxxhhjRozHqBljjDEjxoGaMcYYM2IcqBljjDEjxoGaMcYYM2IcqBljjDEjxoGaMcYYM2IcqBljjDEjxoGaMcYYM2IcqBljOW716tVwcHDI9nFkMhl27NiR7eMwZso4UDPGTFbjxo3x3XffSd0MxnIVB2rGGGPMiHGgZsxM/fvvv6hUqRKsra1RoEAB+Pj4ICYmRvf4ypUrUaFCBahUKri7u2PYsGG6x3777TdUqlQJtra28PT0xJAhQxAdHZ3u6+3cuRPVq1eHlZUVSpQogWnTpiE5OVn3+N27d9GwYUNYWVmhfPnyOHToULrH6927N44fP44//vgDMpkMMpkMjx49ytqHwZgRs5C6AYwxwwsODkbXrl0xZ84cfPHFF4iKisLJkyd19YUXL16MkSNHYvbs2WjZsiUiIiJw+vRp3fPlcjnmz5+P4sWL48GDBxgyZAh++OEHLFq0KNXXO3nyJHr27In58+ejQYMGuH//PgYMGAAAmDJlCjQaDb788ku4urrC398fERERH+3S/uOPP3Dnzh1UrFgRP/30EwCgYMGCOfDpMGZksl1/izFmcgICAgQA8ejRo1Qf9/DwEBMmTMjw8bZs2SIKFCigu79q1Sphb2+vu9+0aVMxc+bMFM/5559/hLu7uxBCiAMHDggLCwvx7Nkz3eP79u0TAMT27dvTfN1GjRqJESNGZLidjJkiPqNmzAxVqVIFTZs2RaVKldC8eXM0a9YMHTt2hKOjI168eIHnz5+jadOmaT7/8OHDmDVrFm7fvo3IyEgkJycjPj4esbGxsLGx+WD/K1eu4PTp05gxY4Zum1qt1j3n1q1b8PT0hIeHh+5xb2/vnH3TjJkoHqNmzAwpFAocOnQI+/btQ/ny5bFgwQKULVsWDx8+hLW1dbrPffToEdq0aYPKlStj69atCAgIwMKFCwEAiYmJqT4nOjoa06ZNw+XLl3WXa9eu4e7du7Cyssrx98dYXsKBmjEzJZPJUL9+fUybNg2XLl2CUqnE9u3bkT9/fhQrVgy+vr6pPi8gIAAajQa//vor6tatizJlyuD58+fpvlb16tURGBiIUqVKfXCRy+Xw8vLC06dPERwcrHvO2bNnP/oelEol1Gp15t44YyaGu74ZM0P+/v7w9fVFs2bN4OLiAn9/f7x8+RJeXl4AgKlTp2LQoEFwcXFBy5YtERUVhdOnT2P48OEoVaoUkpKSsGDBArRt2xanT5/GkiVL0n29yZMno02bNihSpAg6duwIuVyOK1eu4Pr16/j555/h4+ODMmXKoFevXpg7dy4iIyMxYcKEj76PYsWKwd/fH48ePUK+fPng5OQEuZzPP1geI/UgOWPM8G7evCmaN28uChYsKFQqlShTpoxYsGBBin2WLFkiypYtKywtLYW7u7sYPny47rHffvtNuLu7C2tra9G8eXPx999/CwDizZs3QogPJ5MJIcT+/ftFvXr1hLW1tbCzsxO1a9cWf/31l+7xwMBA8cknnwilUinKlCkj9u/f/9HJZIGBgaJu3brC2tpaABAPHz7M7kfDmNGRCfF2PQZjjDHGjA73ETHGGGNGjAM1Y4wxZsQ4UDPGGGNGjAM1Y4wxZsQ4UDPGGGNGjAM1Y4wxZsQ4UDPGGGNGjAM1Y4wxZsQ4UDPGGGNGjAM1Y4wxZsQ4UDPGGGNGjAM1Y4wxZsT+D12oVqQ8We27AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation loss: 0.0004056398441510585\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "# Create the model instance\n",
    "model = MLP_Cond_Memory_Module(treatment_cond=0, memory=3, \n",
    "                                     dim=1, w=256, \n",
    "                                     time_varying=True, \n",
    "                                     conditional=False, \n",
    "                                     lr=1e-3, \n",
    "                                     sigma=0.1, \n",
    "                                     loss_fn=mse_loss, \n",
    "                                     metrics=['mse_loss'], \n",
    "                                     implementation=\"ODE\", \n",
    "                                     sde_noise=0.1, \n",
    "                                     clip=0.01)\n",
    "\n",
    "# Train the model\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "train_model(model, False, train_loader_1d_3m, val_loader_1d_3m, num_epochs=1000, device=device)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "finrl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
